International US Foreign Currency Exchange Rates Predictor

Author: Andrew Tran

Blog Post Inspiration and Objectives

In this blog post, I was hoping to look into using Machine Learning to make a prediction system. In particular, I was hoping to imitate ones used for stock prediction. However, since there were too many tutorials on this topic, I decided to investigate into another global, financial factor that governs trade internationally: international currency rates. In the news recently, I have heard rumors of how the international standard of utilizing the US dollar could be compromised with other global powers such as China and India rise in global dominance. Thus, I focused my efforts on analyzing the US currency conversion rates to other countries. With that said, let’s try to analyze this topic with some Machine Learning:

Data Preprocessing - Cleaning and Analytics

```{python}
# Import needed libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import layers
from copy import deepcopy
import datetime as dt
plt.style.use("fivethirtyeight")
```

First, we will read and display the initial dataset in our file system for this blog post, downloaded from Kaggle. This dataset contains loads of valuable information such as almost every major world power’s international US currency conversion rate.

```{python}
# Reading and displaying the initial dataset
df = pd.read_csv("datasets/foreign_exchange_rates.csv")
df
```

	Unnamed: 0	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5212	5212	2019-12-25	ND	ND	ND	ND	ND	ND	ND	ND	...	ND	ND	ND	ND	ND	ND	ND	ND	ND	ND
5213	5213	2019-12-26	1.4411	0.9007	1.5002	0.7688	4.0602	1.3124	6.9949	7.788	...	1.354	6.7295	109.67	4.1337	8.8799	9.4108	181.3	0.9808	30.11	30.15
5214	5214	2019-12-27	1.4331	0.8949	1.4919	0.7639	4.0507	1.3073	6.9954	7.7874	...	1.352	6.6829	109.47	4.126	8.8291	9.3405	181.35	0.9741	30.09	30.14
5215	5215	2019-12-30	1.4278	0.8915	1.4846	0.761	4.0152	1.3058	6.9864	7.7857	...	1.3483	6.6589	108.85	4.1053	8.7839	9.3145	181.6	0.9677	30.04	29.94
5216	5216	2019-12-31	1.4225	0.8907	1.4826	0.7536	4.019	1.2962	6.9618	7.7894	...	1.3446	6.6554	108.67	4.0918	8.7823	9.3425	181.3	0.9677	29.91	29.75

5217 rows × 24 columns

For clarity on the constraints and parameters of the working datasets, I went to find high-level exploratory statistics on all of the datasets: shape, information about all of the entries, etc.

```{python}
# Determining the shape of the initial dataset
df.shape
```

(5217, 24)

```{python}
# Getting a sample of the initial dataset through the seeing the first 10 entries
# completely in the dataset
df.head()
```

	Unnamed: 0	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3

5 rows × 24 columns

```{python}
# Figuring out all of the columns (and their names) available for me to use in the dataset
df.columns
```

Index(['Unnamed: 0', 'Time Serie', 'AUSTRALIA - AUSTRALIAN DOLLAR/US$',
       'EURO AREA - EURO/US$', 'NEW ZEALAND - NEW ZELAND DOLLAR/US$',
       'UNITED KINGDOM - UNITED KINGDOM POUND/US$', 'BRAZIL - REAL/US$',
       'CANADA - CANADIAN DOLLAR/US$', 'CHINA - YUAN/US$',
       'HONG KONG - HONG KONG DOLLAR/US$', 'INDIA - INDIAN RUPEE/US$',
       'KOREA - WON/US$', 'MEXICO - MEXICAN PESO/US$',
       'SOUTH AFRICA - RAND/US$', 'SINGAPORE - SINGAPORE DOLLAR/US$',
       'DENMARK - DANISH KRONE/US$', 'JAPAN - YEN/US$',
       'MALAYSIA - RINGGIT/US$', 'NORWAY - NORWEGIAN KRONE/US$',
       'SWEDEN - KRONA/US$', 'SRI LANKA - SRI LANKAN RUPEE/US$',
       'SWITZERLAND - FRANC/US$', 'TAIWAN - NEW TAIWAN DOLLAR/US$',
       'THAILAND - BAHT/US$'],
      dtype='object')

```{python}
# Figuring out the number of duplicated elements in the dataset
# (could be problematic if not resolved)
df.duplicated().sum()
```

0

```{python}
# Figuring out the number of 'null'/'NaN' elements in the dataset 
# (if NaN filling is needed or not)
(df.isnull().sum() / df.shape[0]) * 100
```

Unnamed: 0                                   0.0
Time Serie                                   0.0
AUSTRALIA - AUSTRALIAN DOLLAR/US$            0.0
EURO AREA - EURO/US$                         0.0
NEW ZEALAND - NEW ZELAND DOLLAR/US$          0.0
UNITED KINGDOM - UNITED KINGDOM POUND/US$    0.0
BRAZIL - REAL/US$                            0.0
CANADA - CANADIAN DOLLAR/US$                 0.0
CHINA - YUAN/US$                             0.0
HONG KONG - HONG KONG DOLLAR/US$             0.0
INDIA - INDIAN RUPEE/US$                     0.0
KOREA - WON/US$                              0.0
MEXICO - MEXICAN PESO/US$                    0.0
SOUTH AFRICA - RAND/US$                      0.0
SINGAPORE - SINGAPORE DOLLAR/US$             0.0
DENMARK - DANISH KRONE/US$                   0.0
JAPAN - YEN/US$                              0.0
MALAYSIA - RINGGIT/US$                       0.0
NORWAY - NORWEGIAN KRONE/US$                 0.0
SWEDEN - KRONA/US$                           0.0
SRI LANKA - SRI LANKAN RUPEE/US$             0.0
SWITZERLAND - FRANC/US$                      0.0
TAIWAN - NEW TAIWAN DOLLAR/US$               0.0
THAILAND - BAHT/US$                          0.0
dtype: float64

```{python}
# Getting basic information about the dataset
df.info()
```

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5217 entries, 0 to 5216
Data columns (total 24 columns):
 #   Column                                     Non-Null Count  Dtype 
---  ------                                     --------------  ----- 
 0   Unnamed: 0                                 5217 non-null   int64 
 1   Time Serie                                 5217 non-null   object
 2   AUSTRALIA - AUSTRALIAN DOLLAR/US$          5217 non-null   object
 3   EURO AREA - EURO/US$                       5217 non-null   object
 4   NEW ZEALAND - NEW ZELAND DOLLAR/US$        5217 non-null   object
 5   UNITED KINGDOM - UNITED KINGDOM POUND/US$  5217 non-null   object
 6   BRAZIL - REAL/US$                          5217 non-null   object
 7   CANADA - CANADIAN DOLLAR/US$               5217 non-null   object
 8   CHINA - YUAN/US$                           5217 non-null   object
 9   HONG KONG - HONG KONG DOLLAR/US$           5217 non-null   object
 10  INDIA - INDIAN RUPEE/US$                   5217 non-null   object
 11  KOREA - WON/US$                            5217 non-null   object
 12  MEXICO - MEXICAN PESO/US$                  5217 non-null   object
 13  SOUTH AFRICA - RAND/US$                    5217 non-null   object
 14  SINGAPORE - SINGAPORE DOLLAR/US$           5217 non-null   object
 15  DENMARK - DANISH KRONE/US$                 5217 non-null   object
 16  JAPAN - YEN/US$                            5217 non-null   object
 17  MALAYSIA - RINGGIT/US$                     5217 non-null   object
 18  NORWAY - NORWEGIAN KRONE/US$               5217 non-null   object
 19  SWEDEN - KRONA/US$                         5217 non-null   object
 20  SRI LANKA - SRI LANKAN RUPEE/US$           5217 non-null   object
 21  SWITZERLAND - FRANC/US$                    5217 non-null   object
 22  TAIWAN - NEW TAIWAN DOLLAR/US$             5217 non-null   object
 23  THAILAND - BAHT/US$                        5217 non-null   object
dtypes: int64(1), object(23)
memory usage: 978.3+ KB

Additionally, before handing my combined Book dataset over for Machine Learning training and prediction, I need to clean the data prior to the analysis stage: removing duplicates, deleting null/NaN vales, fixing types of columns, filling invalid values with suitable alternatives, etc.

```{python}
# Removing unnecessary/unnamed columns in the dataset
df = df.drop("Unnamed: 0", axis=1)
df
```

	Time Serie	AUSTRALIA - AUSTRALIAN DOLLAR/US$	EURO AREA - EURO/US$	NEW ZEALAND - NEW ZELAND DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	BRAZIL - REAL/US$	CANADA - CANADIAN DOLLAR/US$	CHINA - YUAN/US$	HONG KONG - HONG KONG DOLLAR/US$	INDIA - INDIAN RUPEE/US$	...	SINGAPORE - SINGAPORE DOLLAR/US$	DENMARK - DANISH KRONE/US$	JAPAN - YEN/US$	MALAYSIA - RINGGIT/US$	NORWAY - NORWEGIAN KRONE/US$	SWEDEN - KRONA/US$	SRI LANKA - SRI LANKAN RUPEE/US$	SWITZERLAND - FRANC/US$	TAIWAN - NEW TAIWAN DOLLAR/US$	THAILAND - BAHT/US$
0	2000-01-03	1.5172	0.9847	1.9033	0.6146	1.805	1.4465	8.2798	7.7765	43.55	...	1.6563	7.329	101.7	3.8	7.964	8.443	72.3	1.5808	31.38	36.97
1	2000-01-04	1.5239	0.97	1.9238	0.6109	1.8405	1.4518	8.2799	7.7775	43.55	...	1.6535	7.218	103.09	3.8	7.934	8.36	72.65	1.5565	30.6	37.13
2	2000-01-05	1.5267	0.9676	1.9339	0.6092	1.856	1.4518	8.2798	7.778	43.55	...	1.656	7.208	103.77	3.8	7.935	8.353	72.95	1.5526	30.8	37.1
3	2000-01-06	1.5291	0.9686	1.9436	0.607	1.84	1.4571	8.2797	7.7785	43.55	...	1.6655	7.2125	105.19	3.8	7.94	8.3675	72.95	1.554	31.75	37.62
4	2000-01-07	1.5272	0.9714	1.938	0.6104	1.831	1.4505	8.2794	7.7783	43.55	...	1.6625	7.2285	105.17	3.8	7.966	8.415	73.15	1.5623	30.85	37.3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5212	2019-12-25	ND	ND	ND	ND	ND	ND	ND	ND	ND	...	ND	ND	ND	ND	ND	ND	ND	ND	ND	ND
5213	2019-12-26	1.4411	0.9007	1.5002	0.7688	4.0602	1.3124	6.9949	7.788	71.28	...	1.354	6.7295	109.67	4.1337	8.8799	9.4108	181.3	0.9808	30.11	30.15
5214	2019-12-27	1.4331	0.8949	1.4919	0.7639	4.0507	1.3073	6.9954	7.7874	71.45	...	1.352	6.6829	109.47	4.126	8.8291	9.3405	181.35	0.9741	30.09	30.14
5215	2019-12-30	1.4278	0.8915	1.4846	0.761	4.0152	1.3058	6.9864	7.7857	71.3	...	1.3483	6.6589	108.85	4.1053	8.7839	9.3145	181.6	0.9677	30.04	29.94
5216	2019-12-31	1.4225	0.8907	1.4826	0.7536	4.019	1.2962	6.9618	7.7894	71.36	...	1.3446	6.6554	108.67	4.0918	8.7823	9.3425	181.3	0.9677	29.91	29.75

5217 rows × 23 columns

```{python}
# Converting the date column into a string representation to a parsable
# Datetime object (needed for later)
df = df.rename(columns={"Time Serie": "DATE"})

def str_to_datetime(date_str: str):
    split = tuple(date_str.split("-"))
    year, month, day = int(split[0]), int(split[1]), int(split[2])
    return dt.datetime(year, month, day)

df["DATE"] = df["DATE"].apply(str_to_datetime)
df["DATE"]
```

0      2000-01-03
1      2000-01-04
2      2000-01-05
3      2000-01-06
4      2000-01-07
          ...    
5212   2019-12-25
5213   2019-12-26
5214   2019-12-27
5215   2019-12-30
5216   2019-12-31
Name: DATE, Length: 5217, dtype: datetime64[ns]

As shown below, I had decided to filter out all of the countries except Austrailia, Canada, the United Kingdom, and Switzerland because those countries had the most stable and consistent international currency rates as well as the most comparable (1:1) currency rates with the United States. One additional reason that these countries were selected for this blog post was do the geographical diversity covered most landmass continents across the world, representative of the global market interactions that the United States deals with on a day-to-day basis.

```{python}
# Making the "Date" column the new index (better identifer/key in dataset)
# Taking out other countries except Austrailia, Canada, UK, and 
# Switzerland from dataset for Machine Learning blog post
df.index = df.pop("DATE")
df = df[["AUSTRALIA - AUSTRALIAN DOLLAR/US$", 
         "CANADA - CANADIAN DOLLAR/US$",
         "UNITED KINGDOM - UNITED KINGDOM POUND/US$",
         "SWITZERLAND - FRANC/US$"]]
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.5172	1.4465	0.6146	1.5808
2000-01-04	1.5239	1.4518	0.6109	1.5565
2000-01-05	1.5267	1.4518	0.6092	1.5526
2000-01-06	1.5291	1.4571	0.607	1.554
2000-01-07	1.5272	1.4505	0.6104	1.5623
...	...	...	...	...
2019-12-25	ND	ND	ND	ND
2019-12-26	1.4411	1.3124	0.7688	0.9808
2019-12-27	1.4331	1.3073	0.7639	0.9741
2019-12-30	1.4278	1.3058	0.761	0.9677
2019-12-31	1.4225	1.2962	0.7536	0.9677

5217 rows × 4 columns

```{python}
# Converting of all numerical international currency rates to 2-decimal 
# rates (easier to work with for later)
for col in df:
    df[col] = df[col].map(lambda entry: entry if entry == "ND" else round(float(entry), 2))
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.52	1.45	0.61	1.58
2000-01-04	1.52	1.45	0.61	1.56
2000-01-05	1.53	1.45	0.61	1.55
2000-01-06	1.53	1.46	0.61	1.55
2000-01-07	1.53	1.45	0.61	1.56
...	...	...	...	...
2019-12-25	ND	ND	ND	ND
2019-12-26	1.44	1.31	0.77	0.98
2019-12-27	1.43	1.31	0.76	0.97
2019-12-30	1.43	1.31	0.76	0.97
2019-12-31	1.42	1.3	0.75	0.97

5217 rows × 4 columns

```{python}
# Removing all of the dated rows that contains "ND" as a way to avoid problems 
# when trying to interlay Machine Learning algorithms that require only
# quantitative data with categorical entries
nd_index_list: set[str] = set()
for row_index, _ in df.iterrows():
    for col_entry in df.loc[row_index]:
        if col_entry == "ND":
             nd_index_list.add(row_index)

df = df.drop(labels=list(nd_index_list), axis=0)
df
```

	AUSTRALIA - AUSTRALIAN DOLLAR/US$	CANADA - CANADIAN DOLLAR/US$	UNITED KINGDOM - UNITED KINGDOM POUND/US$	SWITZERLAND - FRANC/US$
DATE
2000-01-03	1.52	1.45	0.61	1.58
2000-01-04	1.52	1.45	0.61	1.56
2000-01-05	1.53	1.45	0.61	1.55
2000-01-06	1.53	1.46	0.61	1.55
2000-01-07	1.53	1.45	0.61	1.56
...	...	...	...	...
2019-12-24	1.44	1.32	0.77	0.98
2019-12-26	1.44	1.31	0.77	0.98
2019-12-27	1.43	1.31	0.76	0.97
2019-12-30	1.43	1.31	0.76	0.97
2019-12-31	1.42	1.3	0.75	0.97

5019 rows × 4 columns

As shown in the code snippet below, with some online references, the df_to_windowed_df function describes a way to use prediction values. A new international currency rate dataframe is created which includes the rows encompassing the 3 previous dates prior to the current date of when the international currency rate was reported as well as the current rate that day. This would be helpful for later Machine Learning predictions because the previous 3 dates and the current will provide it enough context to make more insightful predictions on the future of the international currency rates between that respective country specified and the United States. Later, as you will see, the 3 previous days are going to be the input and the current currency rate (Target) will be our output.

```{python}
def df_to_windowed_df(data_frame: pd.DataFrame, first_date_str: str, last_date_str: str, n: int, col_name: str):
    first_date = str_to_datetime(first_date_str)
    last_date = str_to_datetime(last_date_str)
    
    target_date = first_date
    
    dates: list = []
    X: list = []
    Y: list = []
    
    last_time = False
    while True:
        df_subset = data_frame.loc[:target_date].tail(n + 1)
        
        if (len(df_subset) != n + 1):
            print("Error: Window of size " + str(n) + " is too large for date " + str(target_date))
            return
        
        values = df_subset[col_name].to_numpy()
        x, y = values[:-1], values[-1]
        
        dates.append(target_date)
        X.append(x)
        Y.append(y)
        
        next_week = data_frame.loc[target_date:target_date + dt.timedelta(days=7)]
        next_datetime_str = str(next_week.head(2).tail(1).index.values[0])
        next_date_str = next_datetime_str.split("T")[0]
        year_month_day = next_date_str.split("-")
        year, month, day = year_month_day
        next_date = dt.datetime(year=int(year), month=int(month), day=int(day))
        
        if last_time:
            break
        
        target_date = next_date
        if (target_date == last_date):
            last_time = True
    
    returned_df = pd.DataFrame({})
    returned_df["Target Date"] = dates
    
    X = np.array(X)
    for i in range(0, n):
        X[:, i]
        returned_df["Target-" + str(n - i)] = X[:, i]
    returned_df["Target"] = Y
    
    return returned_df 
```

Thus, with the df_to_windowed_df function created, we created one for every country to later use for Machine Learning model training and eventual predictions of future rates: Austrailia, Canada, the United Kingdom, and Switzerland.

```{python}
# Convert Austrailia's dataframe to a windowed dataframe
windowed_df_austrailia = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="AUSTRALIA - AUSTRALIAN DOLLAR/US$")
windowed_df_austrailia  
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.52	1.52	1.53	1.53
1	2000-01-07	1.52	1.53	1.53	1.53
2	2000-01-10	1.53	1.53	1.53	1.52
3	2000-01-11	1.53	1.53	1.52	1.52
4	2000-01-12	1.53	1.52	1.52	1.52
...	...	...	...	...	...
5011	2019-12-24	1.45	1.45	1.45	1.44
5012	2019-12-26	1.45	1.45	1.44	1.44
5013	2019-12-27	1.45	1.44	1.44	1.43
5014	2019-12-30	1.44	1.44	1.43	1.43
5015	2019-12-31	1.44	1.43	1.43	1.42

5016 rows × 5 columns

```{python}
# Convert Canada's dataframe to a windowed dataframe
windowed_df_canada = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="CANADA - CANADIAN DOLLAR/US$")
windowed_df_canada
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.45	1.45	1.45	1.46
1	2000-01-07	1.45	1.45	1.46	1.45
2	2000-01-10	1.45	1.46	1.45	1.46
3	2000-01-11	1.46	1.45	1.46	1.46
4	2000-01-12	1.45	1.46	1.46	1.46
...	...	...	...	...	...
5011	2019-12-24	1.31	1.32	1.32	1.32
5012	2019-12-26	1.32	1.32	1.32	1.31
5013	2019-12-27	1.32	1.32	1.31	1.31
5014	2019-12-30	1.32	1.31	1.31	1.31
5015	2019-12-31	1.31	1.31	1.31	1.30

5016 rows × 5 columns

```{python}
# Convert United Kingdom's dataframe to a windowed dataframe
windowed_df_united_kingdom = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="UNITED KINGDOM - UNITED KINGDOM POUND/US$")
windowed_df_united_kingdom
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	0.61	0.61	0.61	0.61
1	2000-01-07	0.61	0.61	0.61	0.61
2	2000-01-10	0.61	0.61	0.61	0.61
3	2000-01-11	0.61	0.61	0.61	0.61
4	2000-01-12	0.61	0.61	0.61	0.61
...	...	...	...	...	...
5011	2019-12-24	0.77	0.77	0.77	0.77
5012	2019-12-26	0.77	0.77	0.77	0.77
5013	2019-12-27	0.77	0.77	0.77	0.76
5014	2019-12-30	0.77	0.77	0.76	0.76
5015	2019-12-31	0.77	0.76	0.76	0.75

5016 rows × 5 columns

```{python}
# Convert Switzerland's dataframe to a windowed dataframe
windowed_df_switzerland = df_to_windowed_df(data_frame=df, 
                                first_date_str="2000-01-06",
                                last_date_str="2019-12-31",
                                n=3,
                                col_name="SWITZERLAND - FRANC/US$")
windowed_df_switzerland
```

	Target Date	Target-3	Target-2	Target-1	Target
0	2000-01-06	1.58	1.56	1.55	1.55
1	2000-01-07	1.56	1.55	1.55	1.56
2	2000-01-10	1.55	1.55	1.56	1.57
3	2000-01-11	1.55	1.56	1.57	1.56
4	2000-01-12	1.56	1.57	1.56	1.57
...	...	...	...	...	...
5011	2019-12-24	0.98	0.98	0.98	0.98
5012	2019-12-26	0.98	0.98	0.98	0.98
5013	2019-12-27	0.98	0.98	0.98	0.97
5014	2019-12-30	0.98	0.98	0.97	0.97
5015	2019-12-31	0.98	0.97	0.97	0.97

5016 rows × 5 columns

As the Machine Learning model that will be used later in this blog post only accepts numpy arrays, we need to extract each of the following quantities from each respective country’s dataframe: the dates as an np.array, the X-values for input as an 3-dimensional np.array, and the Y-values for output as an np.array. Thus, with some online references, our codebase now has a function called windowed_df_to_date_X_y as shown below to account for this needed transformation.

```{python}
def windowed_df_to_date_X_y(windowed_dataframe: pd.DataFrame):
    df_as_np = windowed_dataframe.to_numpy()
    
    dates = df_as_np[:, 0]
    
    middle_matrix = df_as_np[:, 1:-1]
    X = middle_matrix.reshape((len(dates), middle_matrix.shape[1], 1))
    Y = df_as_np[:, -1]
    
    return dates, X.astype(np.float32), Y.astype(np.float32)

dates_austrailia, X_austrailia, y_austrailia = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_austrailia)
dates_canada, X_canada, y_canada = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_canada)
dates_united_kingdom, X_united_kingdom, y_united_kingdom = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_united_kingdom)
dates_switzerland, X_switzerland, y_switzerland = windowed_df_to_date_X_y(windowed_dataframe=windowed_df_switzerland)

print(dates_austrailia.shape, X_austrailia.shape, y_austrailia.shape)
print(dates_canada.shape, X_canada.shape, y_canada.shape)
print(dates_united_kingdom.shape, X_united_kingdom.shape, y_united_kingdom.shape)
print(dates_switzerland.shape, X_switzerland.shape, y_switzerland.shape)
len(windowed_df_austrailia), len(windowed_df_canada), len(windowed_df_united_kingdom), len(windowed_df_switzerland)
```

(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)
(5016,) (5016, 3, 1) (5016,)

(5016, 5016, 5016, 5016)

Here, I am trying to a visualization of the cleaned dataset before we pass it over for Machine Learning training and prediction. To show the rate of change for the International Currency Rates for Austrailia, Canada, the United Kingdom, and Switzerland over the 20-year period as collected in the data set, I put together a line graph as shown below.

```{python}
# Plotting the each four countries currency rates (per US dollar) from 
# 2000 - 2020
plt.plot(df.index, df["AUSTRALIA - AUSTRALIAN DOLLAR/US$"], label="AUS to USD", linewidth=1)
plt.plot(df.index, df[ "CANADA - CANADIAN DOLLAR/US$"], label="CAN to USD", linewidth=1)
plt.plot(df.index, df["UNITED KINGDOM - UNITED KINGDOM POUND/US$"], label="GBR to USD", linewidth=1)
plt.plot(df.index, df["SWITZERLAND - FRANC/US$"], label="CHE to USD", linewidth=1)

plt.legend(loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date", fontsize=12)
plt.ylabel("Currency Rate of the Respective Country to USD", fontsize=12)
plt.title("Culmination of the All Four Countries' Datasets - After Parsing But Before Machine Learning Alterations")
plt.show()
```

Machine Learning - Model Training and Evaluation

Great, now we are onto the Machine Learning part of the blog post!

For this blog post, I worked to create Machine Learning models for each country’s dataset separately. I started with Austrailia’s data first (as shown below):

```{python}
# Getting the location of the 80th and 90th percentile of the number of points 
# in the dataset for later train, vailidation, and test category splitting
percentile_80 = int(len(windowed_df_austrailia) * 0.8)
percentile_90 = int(len(windowed_df_austrailia) * 0.9)
percentile_80, percentile_90
```

(4012, 4514)

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Austrailia’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively. This split should be acceptable because I did not mix-and-match training & validation datasets with my testing datasets, wihch would invalidate my entire Machine Learning model. Additionally, looking at the presented in the validation and testing datasets, the range of these respective datasets have been reached before in my training dataset, meaning I anticipated my model should be able to reasonably predict these expected ranges with considerable accuracy.

```{python}
# Splitting Austrailia's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_austrailia_train, X_austrailia_train, y_austrailia_train = dates_austrailia[:percentile_80], X_austrailia[:percentile_80], y_austrailia[:percentile_80]
dates_austrailia_val, X_austrailia_val, y_austrailia_val = dates_austrailia[percentile_80:percentile_90], X_austrailia[percentile_80:percentile_90], y_austrailia[percentile_80:percentile_90]
dates_austrailia_test, X_austrailia_test, y_austrailia_test = dates_austrailia[percentile_90:], X_austrailia[percentile_90:], y_austrailia[percentile_90:]

plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Austrailia's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Austrailia
austrailia_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

austrailia_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

austrailia_model.fit(X_austrailia_train, y_austrailia_train, validation_data=(X_austrailia_val, y_austrailia_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:49 - loss: 1.9734 - mean_absolute_error: 1.3728 22/126 [====>.........................] - ETA: 0s - loss: 1.3943 - mean_absolute_error: 1.1414   47/126 [==========>...................] - ETA: 0s - loss: 0.7960 - mean_absolute_error: 0.7599 72/126 [================>.............] - ETA: 0s - loss: 0.5267 - mean_absolute_error: 0.5350 97/126 [======================>.......] - ETA: 0s - loss: 0.3929 - mean_absolute_error: 0.4163122/126 [============================>.] - ETA: 0s - loss: 0.3135 - mean_absolute_error: 0.3434126/126 [==============================] - 3s 6ms/step - loss: 0.3051 - mean_absolute_error: 0.3355 - val_loss: 3.9797e-04 - val_mean_absolute_error: 0.0172
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0048 - mean_absolute_error: 0.0625 23/126 [====>.........................] - ETA: 0s - loss: 0.0039 - mean_absolute_error: 0.0525 46/126 [=========>....................] - ETA: 0s - loss: 0.0035 - mean_absolute_error: 0.0497 68/126 [===============>..............] - ETA: 0s - loss: 0.0031 - mean_absolute_error: 0.0469 92/126 [====================>.........] - ETA: 0s - loss: 0.0028 - mean_absolute_error: 0.0438119/126 [===========================>..] - ETA: 0s - loss: 0.0024 - mean_absolute_error: 0.0406126/126 [==============================] - 0s 2ms/step - loss: 0.0024 - mean_absolute_error: 0.0399 - val_loss: 1.6066e-04 - val_mean_absolute_error: 0.0103
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0303 23/126 [====>.........................] - ETA: 0s - loss: 8.6128e-04 - mean_absolute_error: 0.0242 49/126 [==========>...................] - ETA: 0s - loss: 7.5785e-04 - mean_absolute_error: 0.0224 73/126 [================>.............] - ETA: 0s - loss: 6.7276e-04 - mean_absolute_error: 0.0208 99/126 [======================>.......] - ETA: 0s - loss: 5.9200e-04 - mean_absolute_error: 0.0193124/126 [============================>.] - ETA: 0s - loss: 5.4251e-04 - mean_absolute_error: 0.0182126/126 [==============================] - 0s 2ms/step - loss: 5.4054e-04 - mean_absolute_error: 0.0181 - val_loss: 1.3494e-04 - val_mean_absolute_error: 0.0090
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 3.2182e-04 - mean_absolute_error: 0.0142 18/126 [===>..........................] - ETA: 0s - loss: 2.8738e-04 - mean_absolute_error: 0.0129 38/126 [========>.....................] - ETA: 0s - loss: 2.6581e-04 - mean_absolute_error: 0.0126 62/126 [=============>................] - ETA: 0s - loss: 2.7329e-04 - mean_absolute_error: 0.0126 87/126 [===================>..........] - ETA: 0s - loss: 2.8504e-04 - mean_absolute_error: 0.0126112/126 [=========================>....] - ETA: 0s - loss: 2.8659e-04 - mean_absolute_error: 0.0125126/126 [==============================] - 0s 3ms/step - loss: 2.8368e-04 - mean_absolute_error: 0.0125 - val_loss: 1.4282e-04 - val_mean_absolute_error: 0.0093
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 6.5165e-04 - mean_absolute_error: 0.0158 24/126 [====>.........................] - ETA: 0s - loss: 3.6778e-04 - mean_absolute_error: 0.0130 50/126 [==========>...................] - ETA: 0s - loss: 3.0182e-04 - mean_absolute_error: 0.0125 75/126 [================>.............] - ETA: 0s - loss: 2.8030e-04 - mean_absolute_error: 0.0122 99/126 [======================>.......] - ETA: 0s - loss: 2.7450e-04 - mean_absolute_error: 0.0121125/126 [============================>.] - ETA: 0s - loss: 2.6800e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6785e-04 - mean_absolute_error: 0.0119 - val_loss: 1.3098e-04 - val_mean_absolute_error: 0.0089
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 2.0439e-04 - mean_absolute_error: 0.0112 25/126 [====>.........................] - ETA: 0s - loss: 2.5072e-04 - mean_absolute_error: 0.0121 51/126 [===========>..................] - ETA: 0s - loss: 2.6118e-04 - mean_absolute_error: 0.0119 75/126 [================>.............] - ETA: 0s - loss: 2.7124e-04 - mean_absolute_error: 0.0120100/126 [======================>.......] - ETA: 0s - loss: 2.7441e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7248e-04 - mean_absolute_error: 0.0120 - val_loss: 1.4727e-04 - val_mean_absolute_error: 0.0095
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 1.7711e-04 - mean_absolute_error: 0.0105 24/126 [====>.........................] - ETA: 0s - loss: 2.7358e-04 - mean_absolute_error: 0.0122 50/126 [==========>...................] - ETA: 0s - loss: 2.5428e-04 - mean_absolute_error: 0.0119 74/126 [================>.............] - ETA: 0s - loss: 2.5947e-04 - mean_absolute_error: 0.0120 96/126 [=====================>........] - ETA: 0s - loss: 2.6404e-04 - mean_absolute_error: 0.0120122/126 [============================>.] - ETA: 0s - loss: 2.7051e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7419e-04 - mean_absolute_error: 0.0120 - val_loss: 1.6695e-04 - val_mean_absolute_error: 0.0101
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 1.8258e-04 - mean_absolute_error: 0.0114 25/126 [====>.........................] - ETA: 0s - loss: 2.8696e-04 - mean_absolute_error: 0.0122 52/126 [===========>..................] - ETA: 0s - loss: 2.8437e-04 - mean_absolute_error: 0.0120 76/126 [=================>............] - ETA: 0s - loss: 2.8158e-04 - mean_absolute_error: 0.0119101/126 [=======================>......] - ETA: 0s - loss: 2.7222e-04 - mean_absolute_error: 0.0119122/126 [============================>.] - ETA: 0s - loss: 2.7349e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7059e-04 - mean_absolute_error: 0.0120 - val_loss: 1.3505e-04 - val_mean_absolute_error: 0.0090
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 2.2175e-04 - mean_absolute_error: 0.0110 23/126 [====>.........................] - ETA: 0s - loss: 2.3464e-04 - mean_absolute_error: 0.0113 49/126 [==========>...................] - ETA: 0s - loss: 2.6646e-04 - mean_absolute_error: 0.0118 75/126 [================>.............] - ETA: 0s - loss: 2.6011e-04 - mean_absolute_error: 0.0118101/126 [=======================>......] - ETA: 0s - loss: 2.6611e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6541e-04 - mean_absolute_error: 0.0118 - val_loss: 2.1705e-04 - val_mean_absolute_error: 0.0118
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 4.2326e-04 - mean_absolute_error: 0.0130 22/126 [====>.........................] - ETA: 0s - loss: 3.2720e-04 - mean_absolute_error: 0.0128 48/126 [==========>...................] - ETA: 0s - loss: 2.8364e-04 - mean_absolute_error: 0.0121 74/126 [================>.............] - ETA: 0s - loss: 2.8080e-04 - mean_absolute_error: 0.0120 98/126 [======================>.......] - ETA: 0s - loss: 2.7308e-04 - mean_absolute_error: 0.0119117/126 [==========================>...] - ETA: 0s - loss: 2.7072e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 3ms/step - loss: 2.7371e-04 - mean_absolute_error: 0.0120 - val_loss: 1.2563e-04 - val_mean_absolute_error: 0.0088
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 2.0411e-04 - mean_absolute_error: 0.0115 25/126 [====>.........................] - ETA: 0s - loss: 2.4451e-04 - mean_absolute_error: 0.0117 51/126 [===========>..................] - ETA: 0s - loss: 2.6110e-04 - mean_absolute_error: 0.0120 76/126 [=================>............] - ETA: 0s - loss: 2.7357e-04 - mean_absolute_error: 0.0120102/126 [=======================>......] - ETA: 0s - loss: 2.7543e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.6997e-04 - mean_absolute_error: 0.0119 - val_loss: 1.3734e-04 - val_mean_absolute_error: 0.0091
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 2.0522e-04 - mean_absolute_error: 0.0115 19/126 [===>..........................] - ETA: 0s - loss: 2.5249e-04 - mean_absolute_error: 0.0118 44/126 [=========>....................] - ETA: 0s - loss: 2.4317e-04 - mean_absolute_error: 0.0117 69/126 [===============>..............] - ETA: 0s - loss: 2.5942e-04 - mean_absolute_error: 0.0118 96/126 [=====================>........] - ETA: 0s - loss: 2.6877e-04 - mean_absolute_error: 0.0118123/126 [============================>.] - ETA: 0s - loss: 2.6656e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6711e-04 - mean_absolute_error: 0.0118 - val_loss: 1.2960e-04 - val_mean_absolute_error: 0.0089
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 1.9772e-04 - mean_absolute_error: 0.0112 20/126 [===>..........................] - ETA: 0s - loss: 2.9496e-04 - mean_absolute_error: 0.0123 45/126 [=========>....................] - ETA: 0s - loss: 3.0058e-04 - mean_absolute_error: 0.0125 72/126 [================>.............] - ETA: 0s - loss: 2.8230e-04 - mean_absolute_error: 0.0121 98/126 [======================>.......] - ETA: 0s - loss: 2.7811e-04 - mean_absolute_error: 0.0120124/126 [============================>.] - ETA: 0s - loss: 2.6808e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6722e-04 - mean_absolute_error: 0.0119 - val_loss: 1.2718e-04 - val_mean_absolute_error: 0.0088
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 3.3532e-04 - mean_absolute_error: 0.0135 17/126 [===>..........................] - ETA: 0s - loss: 3.5962e-04 - mean_absolute_error: 0.0131 38/126 [========>.....................] - ETA: 0s - loss: 3.0196e-04 - mean_absolute_error: 0.0122 65/126 [==============>...............] - ETA: 0s - loss: 2.7197e-04 - mean_absolute_error: 0.0117 91/126 [====================>.........] - ETA: 0s - loss: 2.7580e-04 - mean_absolute_error: 0.0118117/126 [==========================>...] - ETA: 0s - loss: 2.6610e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 3ms/step - loss: 2.6481e-04 - mean_absolute_error: 0.0118 - val_loss: 1.3093e-04 - val_mean_absolute_error: 0.0089
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 2.1035e-04 - mean_absolute_error: 0.0104 24/126 [====>.........................] - ETA: 0s - loss: 2.2464e-04 - mean_absolute_error: 0.0110 44/126 [=========>....................] - ETA: 0s - loss: 2.4095e-04 - mean_absolute_error: 0.0114 65/126 [==============>...............] - ETA: 0s - loss: 2.4743e-04 - mean_absolute_error: 0.0115 90/126 [====================>.........] - ETA: 0s - loss: 2.6430e-04 - mean_absolute_error: 0.0117115/126 [==========================>...] - ETA: 0s - loss: 2.6406e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 3ms/step - loss: 2.6297e-04 - mean_absolute_error: 0.0117 - val_loss: 2.2650e-04 - val_mean_absolute_error: 0.0121
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 4.4781e-04 - mean_absolute_error: 0.0153 22/126 [====>.........................] - ETA: 0s - loss: 2.9127e-04 - mean_absolute_error: 0.0119 48/126 [==========>...................] - ETA: 0s - loss: 3.1136e-04 - mean_absolute_error: 0.0124 74/126 [================>.............] - ETA: 0s - loss: 2.8144e-04 - mean_absolute_error: 0.0121 99/126 [======================>.......] - ETA: 0s - loss: 2.8000e-04 - mean_absolute_error: 0.0121124/126 [============================>.] - ETA: 0s - loss: 2.7552e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.7497e-04 - mean_absolute_error: 0.0121 - val_loss: 1.4424e-04 - val_mean_absolute_error: 0.0097
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 1.9521e-04 - mean_absolute_error: 0.0114 19/126 [===>..........................] - ETA: 0s - loss: 3.1759e-04 - mean_absolute_error: 0.0125 43/126 [=========>....................] - ETA: 0s - loss: 2.7030e-04 - mean_absolute_error: 0.0121 70/126 [===============>..............] - ETA: 0s - loss: 2.7567e-04 - mean_absolute_error: 0.0121 96/126 [=====================>........] - ETA: 0s - loss: 2.6577e-04 - mean_absolute_error: 0.0120122/126 [============================>.] - ETA: 0s - loss: 2.7267e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7060e-04 - mean_absolute_error: 0.0119 - val_loss: 1.2552e-04 - val_mean_absolute_error: 0.0088
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 1.7047e-04 - mean_absolute_error: 0.0105 19/126 [===>..........................] - ETA: 0s - loss: 2.9380e-04 - mean_absolute_error: 0.0124 37/126 [=======>......................] - ETA: 0s - loss: 2.7409e-04 - mean_absolute_error: 0.0122 58/126 [============>.................] - ETA: 0s - loss: 2.8370e-04 - mean_absolute_error: 0.0123 82/126 [==================>...........] - ETA: 0s - loss: 2.9611e-04 - mean_absolute_error: 0.0124108/126 [========================>.....] - ETA: 0s - loss: 2.8737e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 3ms/step - loss: 2.8057e-04 - mean_absolute_error: 0.0122 - val_loss: 1.3462e-04 - val_mean_absolute_error: 0.0090
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 1.9024e-04 - mean_absolute_error: 0.0109 22/126 [====>.........................] - ETA: 0s - loss: 3.1790e-04 - mean_absolute_error: 0.0128 47/126 [==========>...................] - ETA: 0s - loss: 2.8091e-04 - mean_absolute_error: 0.0122 73/126 [================>.............] - ETA: 0s - loss: 2.7359e-04 - mean_absolute_error: 0.0119100/126 [======================>.......] - ETA: 0s - loss: 2.7145e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6410e-04 - mean_absolute_error: 0.0117 - val_loss: 1.4081e-04 - val_mean_absolute_error: 0.0095
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 3.9457e-04 - mean_absolute_error: 0.0145 19/126 [===>..........................] - ETA: 0s - loss: 2.4722e-04 - mean_absolute_error: 0.0113 40/126 [========>.....................] - ETA: 0s - loss: 2.5611e-04 - mean_absolute_error: 0.0115 66/126 [==============>...............] - ETA: 0s - loss: 2.6559e-04 - mean_absolute_error: 0.0116 90/126 [====================>.........] - ETA: 0s - loss: 2.5352e-04 - mean_absolute_error: 0.0115116/126 [==========================>...] - ETA: 0s - loss: 2.5507e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 3ms/step - loss: 2.5994e-04 - mean_absolute_error: 0.0117 - val_loss: 2.4981e-04 - val_mean_absolute_error: 0.0129
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 1.8072e-04 - mean_absolute_error: 0.0111 24/126 [====>.........................] - ETA: 0s - loss: 3.2148e-04 - mean_absolute_error: 0.0135 43/126 [=========>....................] - ETA: 0s - loss: 2.9396e-04 - mean_absolute_error: 0.0127 60/126 [=============>................] - ETA: 0s - loss: 2.9896e-04 - mean_absolute_error: 0.0126 79/126 [=================>............] - ETA: 0s - loss: 2.9402e-04 - mean_absolute_error: 0.0124 97/126 [======================>.......] - ETA: 0s - loss: 2.8643e-04 - mean_absolute_error: 0.0122117/126 [==========================>...] - ETA: 0s - loss: 2.7722e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 3ms/step - loss: 2.7322e-04 - mean_absolute_error: 0.0120 - val_loss: 1.4117e-04 - val_mean_absolute_error: 0.0092
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 3.1723e-04 - mean_absolute_error: 0.0136 25/126 [====>.........................] - ETA: 0s - loss: 2.8954e-04 - mean_absolute_error: 0.0122 48/126 [==========>...................] - ETA: 0s - loss: 2.9471e-04 - mean_absolute_error: 0.0125 72/126 [================>.............] - ETA: 0s - loss: 2.8313e-04 - mean_absolute_error: 0.0123 96/126 [=====================>........] - ETA: 0s - loss: 2.6753e-04 - mean_absolute_error: 0.0120120/126 [===========================>..] - ETA: 0s - loss: 2.7816e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7788e-04 - mean_absolute_error: 0.0120 - val_loss: 1.2982e-04 - val_mean_absolute_error: 0.0089
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 1.3493e-04 - mean_absolute_error: 0.0091 22/126 [====>.........................] - ETA: 0s - loss: 2.5158e-04 - mean_absolute_error: 0.0114 44/126 [=========>....................] - ETA: 0s - loss: 2.6064e-04 - mean_absolute_error: 0.0117 68/126 [===============>..............] - ETA: 0s - loss: 2.6653e-04 - mean_absolute_error: 0.0118 91/126 [====================>.........] - ETA: 0s - loss: 2.8176e-04 - mean_absolute_error: 0.0123117/126 [==========================>...] - ETA: 0s - loss: 2.8460e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 3ms/step - loss: 2.8013e-04 - mean_absolute_error: 0.0122 - val_loss: 1.3502e-04 - val_mean_absolute_error: 0.0093
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 2.0560e-04 - mean_absolute_error: 0.0119 25/126 [====>.........................] - ETA: 0s - loss: 2.2955e-04 - mean_absolute_error: 0.0110 50/126 [==========>...................] - ETA: 0s - loss: 2.7000e-04 - mean_absolute_error: 0.0120 73/126 [================>.............] - ETA: 0s - loss: 2.7580e-04 - mean_absolute_error: 0.0121 99/126 [======================>.......] - ETA: 0s - loss: 2.7376e-04 - mean_absolute_error: 0.0120125/126 [============================>.] - ETA: 0s - loss: 2.7450e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7486e-04 - mean_absolute_error: 0.0120 - val_loss: 1.5379e-04 - val_mean_absolute_error: 0.0101
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 3.6339e-04 - mean_absolute_error: 0.0159 25/126 [====>.........................] - ETA: 0s - loss: 2.6443e-04 - mean_absolute_error: 0.0119 50/126 [==========>...................] - ETA: 0s - loss: 2.5612e-04 - mean_absolute_error: 0.0121 76/126 [=================>............] - ETA: 0s - loss: 2.5444e-04 - mean_absolute_error: 0.0121100/126 [======================>.......] - ETA: 0s - loss: 2.6649e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.9203e-04 - mean_absolute_error: 0.0126 - val_loss: 1.7672e-04 - val_mean_absolute_error: 0.0109
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 2.5561e-04 - mean_absolute_error: 0.0126 21/126 [====>.........................] - ETA: 0s - loss: 2.5099e-04 - mean_absolute_error: 0.0119 46/126 [=========>....................] - ETA: 0s - loss: 2.4716e-04 - mean_absolute_error: 0.0115 68/126 [===============>..............] - ETA: 0s - loss: 2.5241e-04 - mean_absolute_error: 0.0116 94/126 [=====================>........] - ETA: 0s - loss: 2.5741e-04 - mean_absolute_error: 0.0116121/126 [===========================>..] - ETA: 0s - loss: 2.5987e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.6321e-04 - mean_absolute_error: 0.0116 - val_loss: 2.6086e-04 - val_mean_absolute_error: 0.0135
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 2.5291e-04 - mean_absolute_error: 0.0126 23/126 [====>.........................] - ETA: 0s - loss: 3.0315e-04 - mean_absolute_error: 0.0125 50/126 [==========>...................] - ETA: 0s - loss: 2.7499e-04 - mean_absolute_error: 0.0121 76/126 [=================>............] - ETA: 0s - loss: 2.8910e-04 - mean_absolute_error: 0.0122 97/126 [======================>.......] - ETA: 0s - loss: 2.8085e-04 - mean_absolute_error: 0.0121114/126 [==========================>...] - ETA: 0s - loss: 2.8553e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 3ms/step - loss: 2.7920e-04 - mean_absolute_error: 0.0121 - val_loss: 1.8376e-04 - val_mean_absolute_error: 0.0111
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 2.5188e-04 - mean_absolute_error: 0.0136 26/126 [=====>........................] - ETA: 0s - loss: 2.3664e-04 - mean_absolute_error: 0.0112 53/126 [===========>..................] - ETA: 0s - loss: 2.2697e-04 - mean_absolute_error: 0.0113 79/126 [=================>............] - ETA: 0s - loss: 2.4994e-04 - mean_absolute_error: 0.0115104/126 [=======================>......] - ETA: 0s - loss: 2.5231e-04 - mean_absolute_error: 0.0115126/126 [==============================] - 0s 2ms/step - loss: 2.5721e-04 - mean_absolute_error: 0.0116 - val_loss: 1.4128e-04 - val_mean_absolute_error: 0.0095
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 3.3065e-04 - mean_absolute_error: 0.0151 23/126 [====>.........................] - ETA: 0s - loss: 3.0041e-04 - mean_absolute_error: 0.0124 47/126 [==========>...................] - ETA: 0s - loss: 3.0038e-04 - mean_absolute_error: 0.0124 72/126 [================>.............] - ETA: 0s - loss: 2.8983e-04 - mean_absolute_error: 0.0121 93/126 [=====================>........] - ETA: 0s - loss: 2.7808e-04 - mean_absolute_error: 0.0120112/126 [=========================>....] - ETA: 0s - loss: 2.7347e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 3ms/step - loss: 2.7019e-04 - mean_absolute_error: 0.0118 - val_loss: 1.4121e-04 - val_mean_absolute_error: 0.0095
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 1.8536e-04 - mean_absolute_error: 0.0111 23/126 [====>.........................] - ETA: 0s - loss: 4.1735e-04 - mean_absolute_error: 0.0147 49/126 [==========>...................] - ETA: 0s - loss: 3.3726e-04 - mean_absolute_error: 0.0134 76/126 [=================>............] - ETA: 0s - loss: 3.1053e-04 - mean_absolute_error: 0.0128102/126 [=======================>......] - ETA: 0s - loss: 2.9233e-04 - mean_absolute_error: 0.0124125/126 [============================>.] - ETA: 0s - loss: 2.8482e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.8436e-04 - mean_absolute_error: 0.0123 - val_loss: 2.3520e-04 - val_mean_absolute_error: 0.0127
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 2.3643e-04 - mean_absolute_error: 0.0114 25/126 [====>.........................] - ETA: 0s - loss: 2.7953e-04 - mean_absolute_error: 0.0125 50/126 [==========>...................] - ETA: 0s - loss: 2.7116e-04 - mean_absolute_error: 0.0121 74/126 [================>.............] - ETA: 0s - loss: 2.7469e-04 - mean_absolute_error: 0.0125 99/126 [======================>.......] - ETA: 0s - loss: 2.7364e-04 - mean_absolute_error: 0.0124118/126 [===========================>..] - ETA: 0s - loss: 2.8577e-04 - mean_absolute_error: 0.0124126/126 [==============================] - 0s 3ms/step - loss: 2.8200e-04 - mean_absolute_error: 0.0123 - val_loss: 1.4846e-04 - val_mean_absolute_error: 0.0099
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 2.8848e-04 - mean_absolute_error: 0.0120 24/126 [====>.........................] - ETA: 0s - loss: 2.4870e-04 - mean_absolute_error: 0.0120 49/126 [==========>...................] - ETA: 0s - loss: 2.4804e-04 - mean_absolute_error: 0.0117 76/126 [=================>............] - ETA: 0s - loss: 2.7443e-04 - mean_absolute_error: 0.0125102/126 [=======================>......] - ETA: 0s - loss: 2.8713e-04 - mean_absolute_error: 0.0126122/126 [============================>.] - ETA: 0s - loss: 2.9701e-04 - mean_absolute_error: 0.0127126/126 [==============================] - 0s 2ms/step - loss: 2.9700e-04 - mean_absolute_error: 0.0127 - val_loss: 1.2898e-04 - val_mean_absolute_error: 0.0091
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 2.2526e-04 - mean_absolute_error: 0.0119 24/126 [====>.........................] - ETA: 0s - loss: 3.1743e-04 - mean_absolute_error: 0.0122 50/126 [==========>...................] - ETA: 0s - loss: 2.7563e-04 - mean_absolute_error: 0.0119 75/126 [================>.............] - ETA: 0s - loss: 2.8524e-04 - mean_absolute_error: 0.0122101/126 [=======================>......] - ETA: 0s - loss: 2.8085e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.7269e-04 - mean_absolute_error: 0.0120 - val_loss: 2.1459e-04 - val_mean_absolute_error: 0.0121
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 2.8675e-04 - mean_absolute_error: 0.0133 23/126 [====>.........................] - ETA: 0s - loss: 3.2002e-04 - mean_absolute_error: 0.0139 47/126 [==========>...................] - ETA: 0s - loss: 3.1320e-04 - mean_absolute_error: 0.0129 73/126 [================>.............] - ETA: 0s - loss: 2.9944e-04 - mean_absolute_error: 0.0128 99/126 [======================>.......] - ETA: 0s - loss: 3.1620e-04 - mean_absolute_error: 0.0131124/126 [============================>.] - ETA: 0s - loss: 3.0752e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 3.0784e-04 - mean_absolute_error: 0.0130 - val_loss: 1.3464e-04 - val_mean_absolute_error: 0.0093
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 1.8632e-04 - mean_absolute_error: 0.0101 22/126 [====>.........................] - ETA: 0s - loss: 2.9359e-04 - mean_absolute_error: 0.0116 46/126 [=========>....................] - ETA: 0s - loss: 2.6055e-04 - mean_absolute_error: 0.0115 71/126 [===============>..............] - ETA: 0s - loss: 2.5744e-04 - mean_absolute_error: 0.0115 97/126 [======================>.......] - ETA: 0s - loss: 2.6072e-04 - mean_absolute_error: 0.0116122/126 [============================>.] - ETA: 0s - loss: 2.6019e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.5940e-04 - mean_absolute_error: 0.0116 - val_loss: 1.2174e-04 - val_mean_absolute_error: 0.0086
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 1.1436e-04 - mean_absolute_error: 0.0087 22/126 [====>.........................] - ETA: 0s - loss: 2.9434e-04 - mean_absolute_error: 0.0124 48/126 [==========>...................] - ETA: 0s - loss: 2.9534e-04 - mean_absolute_error: 0.0127 75/126 [================>.............] - ETA: 0s - loss: 2.8569e-04 - mean_absolute_error: 0.0125101/126 [=======================>......] - ETA: 0s - loss: 2.7496e-04 - mean_absolute_error: 0.0123125/126 [============================>.] - ETA: 0s - loss: 2.6815e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.6783e-04 - mean_absolute_error: 0.0120 - val_loss: 1.6445e-04 - val_mean_absolute_error: 0.0100
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 1.9117e-04 - mean_absolute_error: 0.0108 22/126 [====>.........................] - ETA: 0s - loss: 2.7307e-04 - mean_absolute_error: 0.0123 45/126 [=========>....................] - ETA: 0s - loss: 3.1747e-04 - mean_absolute_error: 0.0130 72/126 [================>.............] - ETA: 0s - loss: 2.9553e-04 - mean_absolute_error: 0.0124 97/126 [======================>.......] - ETA: 0s - loss: 2.8020e-04 - mean_absolute_error: 0.0122123/126 [============================>.] - ETA: 0s - loss: 2.7728e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.7813e-04 - mean_absolute_error: 0.0122 - val_loss: 1.2328e-04 - val_mean_absolute_error: 0.0087
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 1.9995e-04 - mean_absolute_error: 0.0103 25/126 [====>.........................] - ETA: 0s - loss: 2.8074e-04 - mean_absolute_error: 0.0122 51/126 [===========>..................] - ETA: 0s - loss: 2.6045e-04 - mean_absolute_error: 0.0118 76/126 [=================>............] - ETA: 0s - loss: 2.5306e-04 - mean_absolute_error: 0.0115103/126 [=======================>......] - ETA: 0s - loss: 2.5425e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.7449e-04 - mean_absolute_error: 0.0121 - val_loss: 1.2143e-04 - val_mean_absolute_error: 0.0086
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 2.0617e-04 - mean_absolute_error: 0.0106 19/126 [===>..........................] - ETA: 0s - loss: 2.6215e-04 - mean_absolute_error: 0.0123 39/126 [========>.....................] - ETA: 0s - loss: 2.3988e-04 - mean_absolute_error: 0.0116 65/126 [==============>...............] - ETA: 0s - loss: 2.4412e-04 - mean_absolute_error: 0.0117 91/126 [====================>.........] - ETA: 0s - loss: 2.7116e-04 - mean_absolute_error: 0.0123118/126 [===========================>..] - ETA: 0s - loss: 2.7446e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 3ms/step - loss: 2.8407e-04 - mean_absolute_error: 0.0123 - val_loss: 1.3683e-04 - val_mean_absolute_error: 0.0091
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 2.5152e-04 - mean_absolute_error: 0.0118 24/126 [====>.........................] - ETA: 0s - loss: 2.5759e-04 - mean_absolute_error: 0.0121 50/126 [==========>...................] - ETA: 0s - loss: 2.8604e-04 - mean_absolute_error: 0.0122 72/126 [================>.............] - ETA: 0s - loss: 2.7515e-04 - mean_absolute_error: 0.0119 95/126 [=====================>........] - ETA: 0s - loss: 2.6767e-04 - mean_absolute_error: 0.0117120/126 [===========================>..] - ETA: 0s - loss: 2.6334e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6240e-04 - mean_absolute_error: 0.0117 - val_loss: 1.2110e-04 - val_mean_absolute_error: 0.0087
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 1.3729e-04 - mean_absolute_error: 0.0086 24/126 [====>.........................] - ETA: 0s - loss: 2.0958e-04 - mean_absolute_error: 0.0110 49/126 [==========>...................] - ETA: 0s - loss: 2.5830e-04 - mean_absolute_error: 0.0118 75/126 [================>.............] - ETA: 0s - loss: 2.6473e-04 - mean_absolute_error: 0.0118100/126 [======================>.......] - ETA: 0s - loss: 2.7028e-04 - mean_absolute_error: 0.0117124/126 [============================>.] - ETA: 0s - loss: 2.5976e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.6004e-04 - mean_absolute_error: 0.0116 - val_loss: 1.1966e-04 - val_mean_absolute_error: 0.0085
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 2.2401e-04 - mean_absolute_error: 0.0122 23/126 [====>.........................] - ETA: 0s - loss: 3.0188e-04 - mean_absolute_error: 0.0129 47/126 [==========>...................] - ETA: 0s - loss: 2.6956e-04 - mean_absolute_error: 0.0119 70/126 [===============>..............] - ETA: 0s - loss: 2.7416e-04 - mean_absolute_error: 0.0120 96/126 [=====================>........] - ETA: 0s - loss: 2.7059e-04 - mean_absolute_error: 0.0120121/126 [===========================>..] - ETA: 0s - loss: 2.6952e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6823e-04 - mean_absolute_error: 0.0119 - val_loss: 1.3488e-04 - val_mean_absolute_error: 0.0090
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 1.7016e-04 - mean_absolute_error: 0.0094 26/126 [=====>........................] - ETA: 0s - loss: 2.8572e-04 - mean_absolute_error: 0.0121 52/126 [===========>..................] - ETA: 0s - loss: 2.9969e-04 - mean_absolute_error: 0.0123 76/126 [=================>............] - ETA: 0s - loss: 2.9070e-04 - mean_absolute_error: 0.0123 99/126 [======================>.......] - ETA: 0s - loss: 3.0513e-04 - mean_absolute_error: 0.0129124/126 [============================>.] - ETA: 0s - loss: 3.4106e-04 - mean_absolute_error: 0.0138126/126 [==============================] - 0s 2ms/step - loss: 3.3966e-04 - mean_absolute_error: 0.0137 - val_loss: 3.2687e-04 - val_mean_absolute_error: 0.0153
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 5.2401e-04 - mean_absolute_error: 0.0181 26/126 [=====>........................] - ETA: 0s - loss: 2.9395e-04 - mean_absolute_error: 0.0126 51/126 [===========>..................] - ETA: 0s - loss: 2.7491e-04 - mean_absolute_error: 0.0121 76/126 [=================>............] - ETA: 0s - loss: 2.8254e-04 - mean_absolute_error: 0.0120101/126 [=======================>......] - ETA: 0s - loss: 2.6645e-04 - mean_absolute_error: 0.0118125/126 [============================>.] - ETA: 0s - loss: 2.6192e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6175e-04 - mean_absolute_error: 0.0117 - val_loss: 2.6537e-04 - val_mean_absolute_error: 0.0137
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 4.0939e-04 - mean_absolute_error: 0.0168 25/126 [====>.........................] - ETA: 0s - loss: 3.8507e-04 - mean_absolute_error: 0.0147 49/126 [==========>...................] - ETA: 0s - loss: 3.4815e-04 - mean_absolute_error: 0.0137 74/126 [================>.............] - ETA: 0s - loss: 3.0711e-04 - mean_absolute_error: 0.0129 97/126 [======================>.......] - ETA: 0s - loss: 2.9408e-04 - mean_absolute_error: 0.0126122/126 [============================>.] - ETA: 0s - loss: 3.1239e-04 - mean_absolute_error: 0.0129126/126 [==============================] - 0s 2ms/step - loss: 3.1132e-04 - mean_absolute_error: 0.0129 - val_loss: 4.1802e-04 - val_mean_absolute_error: 0.0180
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 6.5078e-04 - mean_absolute_error: 0.0223 21/126 [====>.........................] - ETA: 0s - loss: 3.1492e-04 - mean_absolute_error: 0.0131 45/126 [=========>....................] - ETA: 0s - loss: 2.7487e-04 - mean_absolute_error: 0.0123 69/126 [===============>..............] - ETA: 0s - loss: 3.0334e-04 - mean_absolute_error: 0.0127 93/126 [=====================>........] - ETA: 0s - loss: 3.0078e-04 - mean_absolute_error: 0.0128117/126 [==========================>...] - ETA: 0s - loss: 2.9372e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 3ms/step - loss: 2.9313e-04 - mean_absolute_error: 0.0126 - val_loss: 2.4016e-04 - val_mean_absolute_error: 0.0127
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 3.8631e-04 - mean_absolute_error: 0.0161 22/126 [====>.........................] - ETA: 0s - loss: 2.3763e-04 - mean_absolute_error: 0.0114 47/126 [==========>...................] - ETA: 0s - loss: 2.9191e-04 - mean_absolute_error: 0.0126 72/126 [================>.............] - ETA: 0s - loss: 3.6391e-04 - mean_absolute_error: 0.0143 96/126 [=====================>........] - ETA: 0s - loss: 3.5455e-04 - mean_absolute_error: 0.0140121/126 [===========================>..] - ETA: 0s - loss: 3.2424e-04 - mean_absolute_error: 0.0133126/126 [==============================] - 0s 2ms/step - loss: 3.2308e-04 - mean_absolute_error: 0.0132 - val_loss: 1.6398e-04 - val_mean_absolute_error: 0.0101
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 8.0947e-04 - mean_absolute_error: 0.0150 25/126 [====>.........................] - ETA: 0s - loss: 2.9596e-04 - mean_absolute_error: 0.0118 51/126 [===========>..................] - ETA: 0s - loss: 3.0824e-04 - mean_absolute_error: 0.0127 75/126 [================>.............] - ETA: 0s - loss: 2.9928e-04 - mean_absolute_error: 0.0127100/126 [======================>.......] - ETA: 0s - loss: 2.8418e-04 - mean_absolute_error: 0.0123122/126 [============================>.] - ETA: 0s - loss: 2.7569e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.7446e-04 - mean_absolute_error: 0.0121 - val_loss: 3.0683e-04 - val_mean_absolute_error: 0.0147
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 4.7590e-04 - mean_absolute_error: 0.0161 26/126 [=====>........................] - ETA: 0s - loss: 2.5967e-04 - mean_absolute_error: 0.0115 50/126 [==========>...................] - ETA: 0s - loss: 2.6412e-04 - mean_absolute_error: 0.0116 74/126 [================>.............] - ETA: 0s - loss: 2.6959e-04 - mean_absolute_error: 0.0120 98/126 [======================>.......] - ETA: 0s - loss: 2.5644e-04 - mean_absolute_error: 0.0117122/126 [============================>.] - ETA: 0s - loss: 2.6295e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6478e-04 - mean_absolute_error: 0.0119 - val_loss: 2.6990e-04 - val_mean_absolute_error: 0.0139
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 3.4413e-04 - mean_absolute_error: 0.0159 22/126 [====>.........................] - ETA: 0s - loss: 2.3136e-04 - mean_absolute_error: 0.0110 46/126 [=========>....................] - ETA: 0s - loss: 2.2332e-04 - mean_absolute_error: 0.0107 70/126 [===============>..............] - ETA: 0s - loss: 2.2930e-04 - mean_absolute_error: 0.0110 94/126 [=====================>........] - ETA: 0s - loss: 2.5708e-04 - mean_absolute_error: 0.0115118/126 [===========================>..] - ETA: 0s - loss: 2.5223e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 3ms/step - loss: 2.5036e-04 - mean_absolute_error: 0.0114 - val_loss: 1.2341e-04 - val_mean_absolute_error: 0.0089
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 2.1597e-04 - mean_absolute_error: 0.0120 25/126 [====>.........................] - ETA: 0s - loss: 2.1728e-04 - mean_absolute_error: 0.0107 50/126 [==========>...................] - ETA: 0s - loss: 2.4839e-04 - mean_absolute_error: 0.0114 74/126 [================>.............] - ETA: 0s - loss: 2.3245e-04 - mean_absolute_error: 0.0111 98/126 [======================>.......] - ETA: 0s - loss: 2.5616e-04 - mean_absolute_error: 0.0115121/126 [===========================>..] - ETA: 0s - loss: 2.5880e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5710e-04 - mean_absolute_error: 0.0116 - val_loss: 1.4243e-04 - val_mean_absolute_error: 0.0097
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 1.5871e-04 - mean_absolute_error: 0.0095 25/126 [====>.........................] - ETA: 0s - loss: 2.9589e-04 - mean_absolute_error: 0.0121 49/126 [==========>...................] - ETA: 0s - loss: 2.8669e-04 - mean_absolute_error: 0.0125 73/126 [================>.............] - ETA: 0s - loss: 2.8368e-04 - mean_absolute_error: 0.0124 98/126 [======================>.......] - ETA: 0s - loss: 2.7075e-04 - mean_absolute_error: 0.0121122/126 [============================>.] - ETA: 0s - loss: 2.7533e-04 - mean_absolute_error: 0.0122126/126 [==============================] - 0s 2ms/step - loss: 2.7576e-04 - mean_absolute_error: 0.0122 - val_loss: 1.2103e-04 - val_mean_absolute_error: 0.0088
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 2.4045e-04 - mean_absolute_error: 0.0118 26/126 [=====>........................] - ETA: 0s - loss: 2.5300e-04 - mean_absolute_error: 0.0124 49/126 [==========>...................] - ETA: 0s - loss: 2.6065e-04 - mean_absolute_error: 0.0121 71/126 [===============>..............] - ETA: 0s - loss: 2.4549e-04 - mean_absolute_error: 0.0117 96/126 [=====================>........] - ETA: 0s - loss: 2.4333e-04 - mean_absolute_error: 0.0115121/126 [===========================>..] - ETA: 0s - loss: 2.5801e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5682e-04 - mean_absolute_error: 0.0117 - val_loss: 1.5355e-04 - val_mean_absolute_error: 0.0101
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 1.1435e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 2.5778e-04 - mean_absolute_error: 0.0115 51/126 [===========>..................] - ETA: 0s - loss: 2.6682e-04 - mean_absolute_error: 0.0118 75/126 [================>.............] - ETA: 0s - loss: 2.7453e-04 - mean_absolute_error: 0.0121 99/126 [======================>.......] - ETA: 0s - loss: 2.7608e-04 - mean_absolute_error: 0.0122124/126 [============================>.] - ETA: 0s - loss: 2.7391e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.7359e-04 - mean_absolute_error: 0.0121 - val_loss: 1.4745e-04 - val_mean_absolute_error: 0.0095
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 1.7662e-04 - mean_absolute_error: 0.0098 24/126 [====>.........................] - ETA: 0s - loss: 2.9083e-04 - mean_absolute_error: 0.0124 47/126 [==========>...................] - ETA: 0s - loss: 2.8291e-04 - mean_absolute_error: 0.0121 71/126 [===============>..............] - ETA: 0s - loss: 2.7431e-04 - mean_absolute_error: 0.0120 95/126 [=====================>........] - ETA: 0s - loss: 2.5449e-04 - mean_absolute_error: 0.0116120/126 [===========================>..] - ETA: 0s - loss: 2.5347e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.5486e-04 - mean_absolute_error: 0.0116 - val_loss: 1.5359e-04 - val_mean_absolute_error: 0.0101
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 2.5950e-04 - mean_absolute_error: 0.0123 26/126 [=====>........................] - ETA: 0s - loss: 2.3459e-04 - mean_absolute_error: 0.0108 51/126 [===========>..................] - ETA: 0s - loss: 2.2946e-04 - mean_absolute_error: 0.0110 73/126 [================>.............] - ETA: 0s - loss: 2.3248e-04 - mean_absolute_error: 0.0110 95/126 [=====================>........] - ETA: 0s - loss: 2.3385e-04 - mean_absolute_error: 0.0110117/126 [==========================>...] - ETA: 0s - loss: 2.4344e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 3ms/step - loss: 2.4662e-04 - mean_absolute_error: 0.0114 - val_loss: 1.1995e-04 - val_mean_absolute_error: 0.0085
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 2.7194e-04 - mean_absolute_error: 0.0131 25/126 [====>.........................] - ETA: 0s - loss: 2.5724e-04 - mean_absolute_error: 0.0118 50/126 [==========>...................] - ETA: 0s - loss: 2.3575e-04 - mean_absolute_error: 0.0113 75/126 [================>.............] - ETA: 0s - loss: 2.8291e-04 - mean_absolute_error: 0.0123100/126 [======================>.......] - ETA: 0s - loss: 2.7247e-04 - mean_absolute_error: 0.0122124/126 [============================>.] - ETA: 0s - loss: 2.7117e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7091e-04 - mean_absolute_error: 0.0120 - val_loss: 1.1796e-04 - val_mean_absolute_error: 0.0086
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 2.5914e-04 - mean_absolute_error: 0.0117 25/126 [====>.........................] - ETA: 0s - loss: 2.4308e-04 - mean_absolute_error: 0.0116 49/126 [==========>...................] - ETA: 0s - loss: 2.8308e-04 - mean_absolute_error: 0.0120 71/126 [===============>..............] - ETA: 0s - loss: 2.5370e-04 - mean_absolute_error: 0.0115 96/126 [=====================>........] - ETA: 0s - loss: 2.7334e-04 - mean_absolute_error: 0.0121120/126 [===========================>..] - ETA: 0s - loss: 2.8328e-04 - mean_absolute_error: 0.0123126/126 [==============================] - 0s 2ms/step - loss: 2.8056e-04 - mean_absolute_error: 0.0122 - val_loss: 1.1993e-04 - val_mean_absolute_error: 0.0084
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 1.3082e-04 - mean_absolute_error: 0.0086 25/126 [====>.........................] - ETA: 0s - loss: 2.3765e-04 - mean_absolute_error: 0.0116 50/126 [==========>...................] - ETA: 0s - loss: 2.9128e-04 - mean_absolute_error: 0.0125 74/126 [================>.............] - ETA: 0s - loss: 3.0149e-04 - mean_absolute_error: 0.0129 96/126 [=====================>........] - ETA: 0s - loss: 2.9342e-04 - mean_absolute_error: 0.0127119/126 [===========================>..] - ETA: 0s - loss: 2.8961e-04 - mean_absolute_error: 0.0126126/126 [==============================] - 0s 2ms/step - loss: 2.8558e-04 - mean_absolute_error: 0.0125 - val_loss: 2.1005e-04 - val_mean_absolute_error: 0.0120
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 2.4973e-04 - mean_absolute_error: 0.0126 25/126 [====>.........................] - ETA: 0s - loss: 4.0302e-04 - mean_absolute_error: 0.0137 50/126 [==========>...................] - ETA: 0s - loss: 3.2778e-04 - mean_absolute_error: 0.0128 75/126 [================>.............] - ETA: 0s - loss: 2.9530e-04 - mean_absolute_error: 0.0121100/126 [======================>.......] - ETA: 0s - loss: 2.8121e-04 - mean_absolute_error: 0.0121125/126 [============================>.] - ETA: 0s - loss: 2.6986e-04 - mean_absolute_error: 0.0118126/126 [==============================] - 0s 2ms/step - loss: 2.6998e-04 - mean_absolute_error: 0.0118 - val_loss: 1.2988e-04 - val_mean_absolute_error: 0.0092
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 3.3046e-04 - mean_absolute_error: 0.0123 26/126 [=====>........................] - ETA: 0s - loss: 2.3787e-04 - mean_absolute_error: 0.0115 51/126 [===========>..................] - ETA: 0s - loss: 2.6845e-04 - mean_absolute_error: 0.0120 76/126 [=================>............] - ETA: 0s - loss: 2.8012e-04 - mean_absolute_error: 0.0122 98/126 [======================>.......] - ETA: 0s - loss: 2.7076e-04 - mean_absolute_error: 0.0120122/126 [============================>.] - ETA: 0s - loss: 2.5871e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.6128e-04 - mean_absolute_error: 0.0117 - val_loss: 2.3832e-04 - val_mean_absolute_error: 0.0129
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 3.0188e-04 - mean_absolute_error: 0.0152 25/126 [====>.........................] - ETA: 0s - loss: 2.0982e-04 - mean_absolute_error: 0.0106 50/126 [==========>...................] - ETA: 0s - loss: 2.2801e-04 - mean_absolute_error: 0.0110 75/126 [================>.............] - ETA: 0s - loss: 2.2431e-04 - mean_absolute_error: 0.0110 97/126 [======================>.......] - ETA: 0s - loss: 2.3444e-04 - mean_absolute_error: 0.0112120/126 [===========================>..] - ETA: 0s - loss: 2.4136e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 3ms/step - loss: 2.4400e-04 - mean_absolute_error: 0.0114 - val_loss: 1.6082e-04 - val_mean_absolute_error: 0.0103
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 2.2997e-04 - mean_absolute_error: 0.0121 25/126 [====>.........................] - ETA: 0s - loss: 3.0447e-04 - mean_absolute_error: 0.0132 49/126 [==========>...................] - ETA: 0s - loss: 3.0728e-04 - mean_absolute_error: 0.0129 74/126 [================>.............] - ETA: 0s - loss: 3.0975e-04 - mean_absolute_error: 0.0131 99/126 [======================>.......] - ETA: 0s - loss: 2.9594e-04 - mean_absolute_error: 0.0128123/126 [============================>.] - ETA: 0s - loss: 2.7863e-04 - mean_absolute_error: 0.0124126/126 [==============================] - 0s 2ms/step - loss: 2.8141e-04 - mean_absolute_error: 0.0124 - val_loss: 3.3444e-04 - val_mean_absolute_error: 0.0157
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 8.3962e-04 - mean_absolute_error: 0.0193 26/126 [=====>........................] - ETA: 0s - loss: 2.7055e-04 - mean_absolute_error: 0.0121 51/126 [===========>..................] - ETA: 0s - loss: 2.7943e-04 - mean_absolute_error: 0.0124 75/126 [================>.............] - ETA: 0s - loss: 2.9185e-04 - mean_absolute_error: 0.0125100/126 [======================>.......] - ETA: 0s - loss: 2.8814e-04 - mean_absolute_error: 0.0123122/126 [============================>.] - ETA: 0s - loss: 2.7024e-04 - mean_absolute_error: 0.0119126/126 [==============================] - 0s 2ms/step - loss: 2.6880e-04 - mean_absolute_error: 0.0119 - val_loss: 1.9547e-04 - val_mean_absolute_error: 0.0115
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 7.7960e-04 - mean_absolute_error: 0.0173 25/126 [====>.........................] - ETA: 0s - loss: 2.7540e-04 - mean_absolute_error: 0.0125 50/126 [==========>...................] - ETA: 0s - loss: 2.9211e-04 - mean_absolute_error: 0.0127 75/126 [================>.............] - ETA: 0s - loss: 2.9278e-04 - mean_absolute_error: 0.0129100/126 [======================>.......] - ETA: 0s - loss: 2.9199e-04 - mean_absolute_error: 0.0129125/126 [============================>.] - ETA: 0s - loss: 2.8919e-04 - mean_absolute_error: 0.0128126/126 [==============================] - 0s 2ms/step - loss: 2.8900e-04 - mean_absolute_error: 0.0128 - val_loss: 1.4137e-04 - val_mean_absolute_error: 0.0096
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 2.2007e-04 - mean_absolute_error: 0.0105 23/126 [====>.........................] - ETA: 0s - loss: 3.2017e-04 - mean_absolute_error: 0.0129 47/126 [==========>...................] - ETA: 0s - loss: 3.1110e-04 - mean_absolute_error: 0.0128 72/126 [================>.............] - ETA: 0s - loss: 2.7561e-04 - mean_absolute_error: 0.0120 97/126 [======================>.......] - ETA: 0s - loss: 2.7781e-04 - mean_absolute_error: 0.0122121/126 [===========================>..] - ETA: 0s - loss: 2.7181e-04 - mean_absolute_error: 0.0120126/126 [==============================] - 0s 2ms/step - loss: 2.7091e-04 - mean_absolute_error: 0.0120 - val_loss: 1.1004e-04 - val_mean_absolute_error: 0.0082
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 1.1739e-04 - mean_absolute_error: 0.0082 26/126 [=====>........................] - ETA: 0s - loss: 2.4184e-04 - mean_absolute_error: 0.0115 51/126 [===========>..................] - ETA: 0s - loss: 2.4661e-04 - mean_absolute_error: 0.0118 75/126 [================>.............] - ETA: 0s - loss: 2.4746e-04 - mean_absolute_error: 0.0116100/126 [======================>.......] - ETA: 0s - loss: 2.4853e-04 - mean_absolute_error: 0.0116125/126 [============================>.] - ETA: 0s - loss: 2.4406e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 2.4375e-04 - mean_absolute_error: 0.0114 - val_loss: 1.0964e-04 - val_mean_absolute_error: 0.0082
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 1.0328e-04 - mean_absolute_error: 0.0086 24/126 [====>.........................] - ETA: 0s - loss: 2.4499e-04 - mean_absolute_error: 0.0111 49/126 [==========>...................] - ETA: 0s - loss: 2.4236e-04 - mean_absolute_error: 0.0110 72/126 [================>.............] - ETA: 0s - loss: 2.2593e-04 - mean_absolute_error: 0.0107 97/126 [======================>.......] - ETA: 0s - loss: 2.2544e-04 - mean_absolute_error: 0.0107121/126 [===========================>..] - ETA: 0s - loss: 2.2898e-04 - mean_absolute_error: 0.0109126/126 [==============================] - 0s 2ms/step - loss: 2.2816e-04 - mean_absolute_error: 0.0109 - val_loss: 1.7447e-04 - val_mean_absolute_error: 0.0108
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 3.7630e-04 - mean_absolute_error: 0.0149 26/126 [=====>........................] - ETA: 0s - loss: 2.4428e-04 - mean_absolute_error: 0.0109 49/126 [==========>...................] - ETA: 0s - loss: 2.4836e-04 - mean_absolute_error: 0.0111 74/126 [================>.............] - ETA: 0s - loss: 2.5359e-04 - mean_absolute_error: 0.0113 96/126 [=====================>........] - ETA: 0s - loss: 2.4781e-04 - mean_absolute_error: 0.0113112/126 [=========================>....] - ETA: 0s - loss: 2.3895e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 3ms/step - loss: 2.4472e-04 - mean_absolute_error: 0.0112 - val_loss: 1.1259e-04 - val_mean_absolute_error: 0.0082
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 4.0621e-04 - mean_absolute_error: 0.0148 24/126 [====>.........................] - ETA: 0s - loss: 2.1053e-04 - mean_absolute_error: 0.0110 49/126 [==========>...................] - ETA: 0s - loss: 2.1934e-04 - mean_absolute_error: 0.0110 73/126 [================>.............] - ETA: 0s - loss: 2.3027e-04 - mean_absolute_error: 0.0112 97/126 [======================>.......] - ETA: 0s - loss: 2.3527e-04 - mean_absolute_error: 0.0112122/126 [============================>.] - ETA: 0s - loss: 2.3868e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3649e-04 - mean_absolute_error: 0.0112 - val_loss: 1.4844e-04 - val_mean_absolute_error: 0.0096
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 1.6651e-04 - mean_absolute_error: 0.0098 23/126 [====>.........................] - ETA: 0s - loss: 2.4808e-04 - mean_absolute_error: 0.0121 47/126 [==========>...................] - ETA: 0s - loss: 2.8960e-04 - mean_absolute_error: 0.0130 72/126 [================>.............] - ETA: 0s - loss: 2.9164e-04 - mean_absolute_error: 0.0128 96/126 [=====================>........] - ETA: 0s - loss: 3.0347e-04 - mean_absolute_error: 0.0131120/126 [===========================>..] - ETA: 0s - loss: 2.9532e-04 - mean_absolute_error: 0.0130126/126 [==============================] - 0s 2ms/step - loss: 2.9404e-04 - mean_absolute_error: 0.0129 - val_loss: 1.0739e-04 - val_mean_absolute_error: 0.0081
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 1.6707e-04 - mean_absolute_error: 0.0100 25/126 [====>.........................] - ETA: 0s - loss: 2.2103e-04 - mean_absolute_error: 0.0112 50/126 [==========>...................] - ETA: 0s - loss: 2.4291e-04 - mean_absolute_error: 0.0110 72/126 [================>.............] - ETA: 0s - loss: 2.4459e-04 - mean_absolute_error: 0.0112 95/126 [=====================>........] - ETA: 0s - loss: 2.5118e-04 - mean_absolute_error: 0.0116120/126 [===========================>..] - ETA: 0s - loss: 2.3951e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.4163e-04 - mean_absolute_error: 0.0113 - val_loss: 1.4151e-04 - val_mean_absolute_error: 0.0096
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 1.8290e-04 - mean_absolute_error: 0.0106 26/126 [=====>........................] - ETA: 0s - loss: 2.5037e-04 - mean_absolute_error: 0.0114 51/126 [===========>..................] - ETA: 0s - loss: 2.4371e-04 - mean_absolute_error: 0.0114 76/126 [=================>............] - ETA: 0s - loss: 2.7904e-04 - mean_absolute_error: 0.0124100/126 [======================>.......] - ETA: 0s - loss: 2.7540e-04 - mean_absolute_error: 0.0121124/126 [============================>.] - ETA: 0s - loss: 2.5595e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5608e-04 - mean_absolute_error: 0.0117 - val_loss: 1.1362e-04 - val_mean_absolute_error: 0.0085
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 2.1293e-04 - mean_absolute_error: 0.0097 25/126 [====>.........................] - ETA: 0s - loss: 2.1421e-04 - mean_absolute_error: 0.0106 49/126 [==========>...................] - ETA: 0s - loss: 2.1221e-04 - mean_absolute_error: 0.0106 72/126 [================>.............] - ETA: 0s - loss: 2.2485e-04 - mean_absolute_error: 0.0110 96/126 [=====================>........] - ETA: 0s - loss: 2.2567e-04 - mean_absolute_error: 0.0110119/126 [===========================>..] - ETA: 0s - loss: 2.4729e-04 - mean_absolute_error: 0.0115126/126 [==============================] - 0s 3ms/step - loss: 2.5133e-04 - mean_absolute_error: 0.0116 - val_loss: 1.1103e-04 - val_mean_absolute_error: 0.0084
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 2.3498e-04 - mean_absolute_error: 0.0109 25/126 [====>.........................] - ETA: 0s - loss: 2.2164e-04 - mean_absolute_error: 0.0107 49/126 [==========>...................] - ETA: 0s - loss: 2.0523e-04 - mean_absolute_error: 0.0105 73/126 [================>.............] - ETA: 0s - loss: 2.1720e-04 - mean_absolute_error: 0.0108 95/126 [=====================>........] - ETA: 0s - loss: 2.3216e-04 - mean_absolute_error: 0.0112119/126 [===========================>..] - ETA: 0s - loss: 2.4159e-04 - mean_absolute_error: 0.0113126/126 [==============================] - 0s 2ms/step - loss: 2.3684e-04 - mean_absolute_error: 0.0112 - val_loss: 1.2870e-04 - val_mean_absolute_error: 0.0091
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 1.9168e-04 - mean_absolute_error: 0.0113 26/126 [=====>........................] - ETA: 0s - loss: 2.2337e-04 - mean_absolute_error: 0.0111 50/126 [==========>...................] - ETA: 0s - loss: 2.1163e-04 - mean_absolute_error: 0.0108 74/126 [================>.............] - ETA: 0s - loss: 2.3773e-04 - mean_absolute_error: 0.0114 98/126 [======================>.......] - ETA: 0s - loss: 2.4873e-04 - mean_absolute_error: 0.0115122/126 [============================>.] - ETA: 0s - loss: 2.4514e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 2.4453e-04 - mean_absolute_error: 0.0114 - val_loss: 1.7285e-04 - val_mean_absolute_error: 0.0105
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 2.7138e-04 - mean_absolute_error: 0.0116 25/126 [====>.........................] - ETA: 0s - loss: 2.3245e-04 - mean_absolute_error: 0.0109 50/126 [==========>...................] - ETA: 0s - loss: 2.2190e-04 - mean_absolute_error: 0.0111 75/126 [================>.............] - ETA: 0s - loss: 2.3996e-04 - mean_absolute_error: 0.0112 98/126 [======================>.......] - ETA: 0s - loss: 2.3381e-04 - mean_absolute_error: 0.0111122/126 [============================>.] - ETA: 0s - loss: 2.3874e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3739e-04 - mean_absolute_error: 0.0112 - val_loss: 1.6155e-04 - val_mean_absolute_error: 0.0101
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 6.6355e-04 - mean_absolute_error: 0.0143 26/126 [=====>........................] - ETA: 0s - loss: 2.6161e-04 - mean_absolute_error: 0.0122 50/126 [==========>...................] - ETA: 0s - loss: 2.3575e-04 - mean_absolute_error: 0.0112 74/126 [================>.............] - ETA: 0s - loss: 2.3370e-04 - mean_absolute_error: 0.0110 99/126 [======================>.......] - ETA: 0s - loss: 2.3673e-04 - mean_absolute_error: 0.0111121/126 [===========================>..] - ETA: 0s - loss: 2.5310e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.5332e-04 - mean_absolute_error: 0.0116 - val_loss: 1.8378e-04 - val_mean_absolute_error: 0.0112
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 1.8143e-04 - mean_absolute_error: 0.0106 25/126 [====>.........................] - ETA: 0s - loss: 2.8166e-04 - mean_absolute_error: 0.0122 50/126 [==========>...................] - ETA: 0s - loss: 2.4576e-04 - mean_absolute_error: 0.0115 74/126 [================>.............] - ETA: 0s - loss: 2.6849e-04 - mean_absolute_error: 0.0121 98/126 [======================>.......] - ETA: 0s - loss: 2.6148e-04 - mean_absolute_error: 0.0119121/126 [===========================>..] - ETA: 0s - loss: 2.5011e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.4943e-04 - mean_absolute_error: 0.0115 - val_loss: 1.5067e-04 - val_mean_absolute_error: 0.0097
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 2.1007e-04 - mean_absolute_error: 0.0119 25/126 [====>.........................] - ETA: 0s - loss: 2.0210e-04 - mean_absolute_error: 0.0108 51/126 [===========>..................] - ETA: 0s - loss: 2.1138e-04 - mean_absolute_error: 0.0106 75/126 [================>.............] - ETA: 0s - loss: 2.2094e-04 - mean_absolute_error: 0.0107 99/126 [======================>.......] - ETA: 0s - loss: 2.2612e-04 - mean_absolute_error: 0.0108121/126 [===========================>..] - ETA: 0s - loss: 2.1997e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1923e-04 - mean_absolute_error: 0.0106 - val_loss: 1.7465e-04 - val_mean_absolute_error: 0.0106
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 2.0912e-04 - mean_absolute_error: 0.0116 25/126 [====>.........................] - ETA: 0s - loss: 3.2684e-04 - mean_absolute_error: 0.0136 49/126 [==========>...................] - ETA: 0s - loss: 2.8860e-04 - mean_absolute_error: 0.0129 73/126 [================>.............] - ETA: 0s - loss: 2.6297e-04 - mean_absolute_error: 0.0123 97/126 [======================>.......] - ETA: 0s - loss: 2.4314e-04 - mean_absolute_error: 0.0117121/126 [===========================>..] - ETA: 0s - loss: 2.5143e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5246e-04 - mean_absolute_error: 0.0118 - val_loss: 1.0255e-04 - val_mean_absolute_error: 0.0079
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 1.1492e-04 - mean_absolute_error: 0.0074 25/126 [====>.........................] - ETA: 0s - loss: 2.7897e-04 - mean_absolute_error: 0.0122 49/126 [==========>...................] - ETA: 0s - loss: 2.4378e-04 - mean_absolute_error: 0.0116 74/126 [================>.............] - ETA: 0s - loss: 2.2877e-04 - mean_absolute_error: 0.0111 98/126 [======================>.......] - ETA: 0s - loss: 2.3142e-04 - mean_absolute_error: 0.0110123/126 [============================>.] - ETA: 0s - loss: 2.3133e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 2ms/step - loss: 2.3252e-04 - mean_absolute_error: 0.0111 - val_loss: 2.0981e-04 - val_mean_absolute_error: 0.0121
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 2.5636e-04 - mean_absolute_error: 0.0136 26/126 [=====>........................] - ETA: 0s - loss: 2.7939e-04 - mean_absolute_error: 0.0127 51/126 [===========>..................] - ETA: 0s - loss: 2.7569e-04 - mean_absolute_error: 0.0125 76/126 [=================>............] - ETA: 0s - loss: 2.5806e-04 - mean_absolute_error: 0.0120101/126 [=======================>......] - ETA: 0s - loss: 2.5666e-04 - mean_absolute_error: 0.0118126/126 [==============================] - ETA: 0s - loss: 2.5171e-04 - mean_absolute_error: 0.0117126/126 [==============================] - 0s 2ms/step - loss: 2.5171e-04 - mean_absolute_error: 0.0117 - val_loss: 4.4660e-04 - val_mean_absolute_error: 0.0188
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 4.1462e-04 - mean_absolute_error: 0.0185 24/126 [====>.........................] - ETA: 0s - loss: 3.3156e-04 - mean_absolute_error: 0.0127 48/126 [==========>...................] - ETA: 0s - loss: 2.7832e-04 - mean_absolute_error: 0.0120 72/126 [================>.............] - ETA: 0s - loss: 2.5015e-04 - mean_absolute_error: 0.0115 96/126 [=====================>........] - ETA: 0s - loss: 2.4812e-04 - mean_absolute_error: 0.0114120/126 [===========================>..] - ETA: 0s - loss: 2.3734e-04 - mean_absolute_error: 0.0111126/126 [==============================] - 0s 2ms/step - loss: 2.3341e-04 - mean_absolute_error: 0.0111 - val_loss: 1.0409e-04 - val_mean_absolute_error: 0.0079
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 9.0391e-05 - mean_absolute_error: 0.0073 25/126 [====>.........................] - ETA: 0s - loss: 2.1458e-04 - mean_absolute_error: 0.0106 47/126 [==========>...................] - ETA: 0s - loss: 2.2768e-04 - mean_absolute_error: 0.0107 70/126 [===============>..............] - ETA: 0s - loss: 2.3579e-04 - mean_absolute_error: 0.0109 94/126 [=====================>........] - ETA: 0s - loss: 2.4095e-04 - mean_absolute_error: 0.0112118/126 [===========================>..] - ETA: 0s - loss: 2.2926e-04 - mean_absolute_error: 0.0110126/126 [==============================] - 0s 3ms/step - loss: 2.2595e-04 - mean_absolute_error: 0.0109 - val_loss: 1.1475e-04 - val_mean_absolute_error: 0.0083
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 1.3383e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 2.0297e-04 - mean_absolute_error: 0.0100 51/126 [===========>..................] - ETA: 0s - loss: 1.9247e-04 - mean_absolute_error: 0.0098 76/126 [=================>............] - ETA: 0s - loss: 2.0985e-04 - mean_absolute_error: 0.0103100/126 [======================>.......] - ETA: 0s - loss: 2.0527e-04 - mean_absolute_error: 0.0102125/126 [============================>.] - ETA: 0s - loss: 2.1539e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1537e-04 - mean_absolute_error: 0.0106 - val_loss: 1.0095e-04 - val_mean_absolute_error: 0.0079
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 1.3735e-04 - mean_absolute_error: 0.0091 24/126 [====>.........................] - ETA: 0s - loss: 2.0206e-04 - mean_absolute_error: 0.0105 48/126 [==========>...................] - ETA: 0s - loss: 2.1880e-04 - mean_absolute_error: 0.0108 73/126 [================>.............] - ETA: 0s - loss: 2.0065e-04 - mean_absolute_error: 0.0104 98/126 [======================>.......] - ETA: 0s - loss: 2.1260e-04 - mean_absolute_error: 0.0107122/126 [============================>.] - ETA: 0s - loss: 2.1793e-04 - mean_absolute_error: 0.0108126/126 [==============================] - 0s 2ms/step - loss: 2.2603e-04 - mean_absolute_error: 0.0109 - val_loss: 9.9247e-05 - val_mean_absolute_error: 0.0077
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 1.4916e-04 - mean_absolute_error: 0.0088 26/126 [=====>........................] - ETA: 0s - loss: 3.8379e-04 - mean_absolute_error: 0.0152 51/126 [===========>..................] - ETA: 0s - loss: 3.2506e-04 - mean_absolute_error: 0.0131 73/126 [================>.............] - ETA: 0s - loss: 2.8935e-04 - mean_absolute_error: 0.0123 97/126 [======================>.......] - ETA: 0s - loss: 2.6892e-04 - mean_absolute_error: 0.0119121/126 [===========================>..] - ETA: 0s - loss: 2.5168e-04 - mean_absolute_error: 0.0115126/126 [==============================] - 0s 2ms/step - loss: 2.4804e-04 - mean_absolute_error: 0.0114 - val_loss: 1.0036e-04 - val_mean_absolute_error: 0.0078
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 9.7792e-05 - mean_absolute_error: 0.0077 25/126 [====>.........................] - ETA: 0s - loss: 2.2201e-04 - mean_absolute_error: 0.0102 49/126 [==========>...................] - ETA: 0s - loss: 2.0698e-04 - mean_absolute_error: 0.0102 73/126 [================>.............] - ETA: 0s - loss: 1.9845e-04 - mean_absolute_error: 0.0102 97/126 [======================>.......] - ETA: 0s - loss: 2.0451e-04 - mean_absolute_error: 0.0102121/126 [===========================>..] - ETA: 0s - loss: 2.0671e-04 - mean_absolute_error: 0.0103126/126 [==============================] - 0s 2ms/step - loss: 2.0700e-04 - mean_absolute_error: 0.0103 - val_loss: 9.9132e-05 - val_mean_absolute_error: 0.0077
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 3.1854e-04 - mean_absolute_error: 0.0111 25/126 [====>.........................] - ETA: 0s - loss: 1.7194e-04 - mean_absolute_error: 0.0099 49/126 [==========>...................] - ETA: 0s - loss: 1.9857e-04 - mean_absolute_error: 0.0106 72/126 [================>.............] - ETA: 0s - loss: 2.1228e-04 - mean_absolute_error: 0.0105 97/126 [======================>.......] - ETA: 0s - loss: 2.2288e-04 - mean_absolute_error: 0.0109120/126 [===========================>..] - ETA: 0s - loss: 2.3353e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 3ms/step - loss: 2.3062e-04 - mean_absolute_error: 0.0111 - val_loss: 1.4851e-04 - val_mean_absolute_error: 0.0097
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 1.1868e-04 - mean_absolute_error: 0.0076 24/126 [====>.........................] - ETA: 0s - loss: 2.1210e-04 - mean_absolute_error: 0.0099 48/126 [==========>...................] - ETA: 0s - loss: 2.0347e-04 - mean_absolute_error: 0.0100 73/126 [================>.............] - ETA: 0s - loss: 2.1668e-04 - mean_absolute_error: 0.0105 95/126 [=====================>........] - ETA: 0s - loss: 2.2126e-04 - mean_absolute_error: 0.0107118/126 [===========================>..] - ETA: 0s - loss: 2.1804e-04 - mean_absolute_error: 0.0107126/126 [==============================] - 0s 3ms/step - loss: 2.1452e-04 - mean_absolute_error: 0.0106 - val_loss: 1.4237e-04 - val_mean_absolute_error: 0.0095
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 1.1319e-04 - mean_absolute_error: 0.0089 25/126 [====>.........................] - ETA: 0s - loss: 2.4627e-04 - mean_absolute_error: 0.0117 50/126 [==========>...................] - ETA: 0s - loss: 2.5020e-04 - mean_absolute_error: 0.0118 74/126 [================>.............] - ETA: 0s - loss: 2.6021e-04 - mean_absolute_error: 0.0118 98/126 [======================>.......] - ETA: 0s - loss: 2.6509e-04 - mean_absolute_error: 0.0121123/126 [============================>.] - ETA: 0s - loss: 2.6150e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.6160e-04 - mean_absolute_error: 0.0121 - val_loss: 9.6221e-05 - val_mean_absolute_error: 0.0076
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 1.1222e-04 - mean_absolute_error: 0.0087 26/126 [=====>........................] - ETA: 0s - loss: 2.3639e-04 - mean_absolute_error: 0.0110 50/126 [==========>...................] - ETA: 0s - loss: 2.2174e-04 - mean_absolute_error: 0.0109 74/126 [================>.............] - ETA: 0s - loss: 2.2458e-04 - mean_absolute_error: 0.0109 96/126 [=====================>........] - ETA: 0s - loss: 2.3070e-04 - mean_absolute_error: 0.0112121/126 [===========================>..] - ETA: 0s - loss: 2.3995e-04 - mean_absolute_error: 0.0114126/126 [==============================] - 0s 2ms/step - loss: 2.3784e-04 - mean_absolute_error: 0.0113 - val_loss: 1.1272e-04 - val_mean_absolute_error: 0.0085
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 1.5273e-04 - mean_absolute_error: 0.0098 25/126 [====>.........................] - ETA: 0s - loss: 2.3884e-04 - mean_absolute_error: 0.0115 50/126 [==========>...................] - ETA: 0s - loss: 2.4751e-04 - mean_absolute_error: 0.0115 75/126 [================>.............] - ETA: 0s - loss: 2.5008e-04 - mean_absolute_error: 0.0115 99/126 [======================>.......] - ETA: 0s - loss: 2.6177e-04 - mean_absolute_error: 0.0120120/126 [===========================>..] - ETA: 0s - loss: 2.4508e-04 - mean_absolute_error: 0.0116126/126 [==============================] - 0s 2ms/step - loss: 2.4135e-04 - mean_absolute_error: 0.0115 - val_loss: 1.4724e-04 - val_mean_absolute_error: 0.0099
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 1.6811e-04 - mean_absolute_error: 0.0104 25/126 [====>.........................] - ETA: 0s - loss: 2.3244e-04 - mean_absolute_error: 0.0114 50/126 [==========>...................] - ETA: 0s - loss: 2.3807e-04 - mean_absolute_error: 0.0111 75/126 [================>.............] - ETA: 0s - loss: 2.3257e-04 - mean_absolute_error: 0.0109100/126 [======================>.......] - ETA: 0s - loss: 2.1965e-04 - mean_absolute_error: 0.0106124/126 [============================>.] - ETA: 0s - loss: 2.1285e-04 - mean_absolute_error: 0.0105126/126 [==============================] - 0s 2ms/step - loss: 2.1151e-04 - mean_absolute_error: 0.0105 - val_loss: 1.0866e-04 - val_mean_absolute_error: 0.0083
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 2.3910e-04 - mean_absolute_error: 0.0116 26/126 [=====>........................] - ETA: 0s - loss: 2.7047e-04 - mean_absolute_error: 0.0116 51/126 [===========>..................] - ETA: 0s - loss: 2.2856e-04 - mean_absolute_error: 0.0108 76/126 [=================>............] - ETA: 0s - loss: 2.2756e-04 - mean_absolute_error: 0.0111101/126 [=======================>......] - ETA: 0s - loss: 2.1970e-04 - mean_absolute_error: 0.0109123/126 [============================>.] - ETA: 0s - loss: 2.3428e-04 - mean_absolute_error: 0.0112126/126 [==============================] - 0s 2ms/step - loss: 2.3554e-04 - mean_absolute_error: 0.0113 - val_loss: 1.9981e-04 - val_mean_absolute_error: 0.0118
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 2.5971e-04 - mean_absolute_error: 0.0122 25/126 [====>.........................] - ETA: 0s - loss: 1.9010e-04 - mean_absolute_error: 0.0101 49/126 [==========>...................] - ETA: 0s - loss: 1.8741e-04 - mean_absolute_error: 0.0097 74/126 [================>.............] - ETA: 0s - loss: 1.9432e-04 - mean_absolute_error: 0.0099 99/126 [======================>.......] - ETA: 0s - loss: 2.0705e-04 - mean_absolute_error: 0.0102124/126 [============================>.] - ETA: 0s - loss: 2.0022e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.9977e-04 - mean_absolute_error: 0.0101 - val_loss: 2.4338e-04 - val_mean_absolute_error: 0.0132
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 1.4210e-04 - mean_absolute_error: 0.0100 23/126 [====>.........................] - ETA: 0s - loss: 2.1255e-04 - mean_absolute_error: 0.0106 47/126 [==========>...................] - ETA: 0s - loss: 2.0551e-04 - mean_absolute_error: 0.0106 71/126 [===============>..............] - ETA: 0s - loss: 2.0424e-04 - mean_absolute_error: 0.0106 95/126 [=====================>........] - ETA: 0s - loss: 2.1718e-04 - mean_absolute_error: 0.0107120/126 [===========================>..] - ETA: 0s - loss: 2.1534e-04 - mean_absolute_error: 0.0106126/126 [==============================] - 0s 2ms/step - loss: 2.1360e-04 - mean_absolute_error: 0.0106 - val_loss: 1.4752e-04 - val_mean_absolute_error: 0.0097
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 3.1826e-04 - mean_absolute_error: 0.0128 25/126 [====>.........................] - ETA: 0s - loss: 2.4802e-04 - mean_absolute_error: 0.0116 50/126 [==========>...................] - ETA: 0s - loss: 2.1089e-04 - mean_absolute_error: 0.0105 75/126 [================>.............] - ETA: 0s - loss: 2.0816e-04 - mean_absolute_error: 0.0104 99/126 [======================>.......] - ETA: 0s - loss: 2.0802e-04 - mean_absolute_error: 0.0104123/126 [============================>.] - ETA: 0s - loss: 2.0251e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 2ms/step - loss: 2.0135e-04 - mean_absolute_error: 0.0102 - val_loss: 1.1595e-04 - val_mean_absolute_error: 0.0085
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 1.3914e-04 - mean_absolute_error: 0.0097 25/126 [====>.........................] - ETA: 0s - loss: 1.9945e-04 - mean_absolute_error: 0.0107 50/126 [==========>...................] - ETA: 0s - loss: 2.0095e-04 - mean_absolute_error: 0.0106 74/126 [================>.............] - ETA: 0s - loss: 2.5056e-04 - mean_absolute_error: 0.0119 98/126 [======================>.......] - ETA: 0s - loss: 2.6962e-04 - mean_absolute_error: 0.0125122/126 [============================>.] - ETA: 0s - loss: 2.6069e-04 - mean_absolute_error: 0.0121126/126 [==============================] - 0s 2ms/step - loss: 2.6102e-04 - mean_absolute_error: 0.0121 - val_loss: 9.2348e-05 - val_mean_absolute_error: 0.0074

<keras.src.callbacks.History at 0x21151fbec50>

After the training and fitting of the Machine Learning model for Austrailia, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Austrailia with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
austrailia_train_pred = austrailia_model.predict(X_austrailia_train).flatten()

plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4)
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Training Set Predictions vs. Observations")
plt.show()

austrailia_val_pred = austrailia_model.predict(X_austrailia_val).flatten()

plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Validation Set Predictions vs. Observations")
plt.show()

austrailia_test_pred = austrailia_model.predict(X_austrailia_test).flatten()

plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Austrailia - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 45s 32/126 [======>.......................] - ETA: 0s  71/126 [===============>..............] - ETA: 0s116/126 [==========================>...] - ETA: 0s126/126 [==============================] - 1s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 2ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Austrailia’s international currency rate with the United States.

```{python}
# Plotting Austrailia's observational (reference) data with the predictions of 
# its Machine Learning Model (as a way to visually inspect the effectiveness of 
# the model) 
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4)
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1)
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4)
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1)
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4)
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations")
plt.show()
```

Once Austraila’s data was completely trained and visualized, I worked on Canada’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Canada’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting Canada's data into train, test, and validation sets on 3 mediums: 
# the X-axis, the y-axis, and the indices (represented by dates)
dates_canada_train, X_canada_train, y_canada_train = dates_canada[:percentile_80], X_canada[:percentile_80], y_canada[:percentile_80]
dates_canada_val, X_canada_val, y_canada_val = dates_canada[percentile_80:percentile_90], X_canada[percentile_80:percentile_90], y_canada[percentile_80:percentile_90]
dates_canada_test, X_canada_test, y_canada_test = dates_canada[percentile_90:], X_canada[percentile_90:], y_canada[percentile_90:]

plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Canada's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Canada
canada_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

canada_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

canada_model.fit(X_canada_train, y_canada_train, validation_data=(X_canada_val, y_canada_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:43 - loss: 1.8081 - mean_absolute_error: 1.3317 21/126 [====>.........................] - ETA: 0s - loss: 1.2760 - mean_absolute_error: 1.1071   46/126 [=========>....................] - ETA: 0s - loss: 0.7308 - mean_absolute_error: 0.7374 71/126 [===============>..............] - ETA: 0s - loss: 0.4798 - mean_absolute_error: 0.5154 96/126 [=====================>........] - ETA: 0s - loss: 0.3558 - mean_absolute_error: 0.3948122/126 [============================>.] - ETA: 0s - loss: 0.2806 - mean_absolute_error: 0.3206126/126 [==============================] - 3s 6ms/step - loss: 0.2731 - mean_absolute_error: 0.3132 - val_loss: 8.7997e-04 - val_mean_absolute_error: 0.0262
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0021 - mean_absolute_error: 0.0377 23/126 [====>.........................] - ETA: 0s - loss: 0.0026 - mean_absolute_error: 0.0441 46/126 [=========>....................] - ETA: 0s - loss: 0.0024 - mean_absolute_error: 0.0423 69/126 [===============>..............] - ETA: 0s - loss: 0.0023 - mean_absolute_error: 0.0414 95/126 [=====================>........] - ETA: 0s - loss: 0.0022 - mean_absolute_error: 0.0406120/126 [===========================>..] - ETA: 0s - loss: 0.0020 - mean_absolute_error: 0.0391126/126 [==============================] - 0s 3ms/step - loss: 0.0020 - mean_absolute_error: 0.0388 - val_loss: 4.8611e-04 - val_mean_absolute_error: 0.0190
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 0.0017 - mean_absolute_error: 0.0368 23/126 [====>.........................] - ETA: 0s - loss: 0.0013 - mean_absolute_error: 0.0309 47/126 [==========>...................] - ETA: 0s - loss: 0.0011 - mean_absolute_error: 0.0292 71/126 [===============>..............] - ETA: 0s - loss: 0.0010 - mean_absolute_error: 0.0278 97/126 [======================>.......] - ETA: 0s - loss: 9.4170e-04 - mean_absolute_error: 0.0265119/126 [===========================>..] - ETA: 0s - loss: 8.6811e-04 - mean_absolute_error: 0.0253126/126 [==============================] - 0s 3ms/step - loss: 8.5053e-04 - mean_absolute_error: 0.0250 - val_loss: 1.9765e-04 - val_mean_absolute_error: 0.0114
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 4.3766e-04 - mean_absolute_error: 0.0190 19/126 [===>..........................] - ETA: 0s - loss: 4.5125e-04 - mean_absolute_error: 0.0183 42/126 [=========>....................] - ETA: 0s - loss: 4.0210e-04 - mean_absolute_error: 0.0172 67/126 [==============>...............] - ETA: 0s - loss: 3.6157e-04 - mean_absolute_error: 0.0161 93/126 [=====================>........] - ETA: 0s - loss: 3.2672e-04 - mean_absolute_error: 0.0152118/126 [===========================>..] - ETA: 0s - loss: 2.9982e-04 - mean_absolute_error: 0.0145126/126 [==============================] - 0s 3ms/step - loss: 2.9354e-04 - mean_absolute_error: 0.0143 - val_loss: 1.0823e-04 - val_mean_absolute_error: 0.0082
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 2.0204e-04 - mean_absolute_error: 0.0116 23/126 [====>.........................] - ETA: 0s - loss: 1.6405e-04 - mean_absolute_error: 0.0104 44/126 [=========>....................] - ETA: 0s - loss: 1.5035e-04 - mean_absolute_error: 0.0099 69/126 [===============>..............] - ETA: 0s - loss: 1.4750e-04 - mean_absolute_error: 0.0098 93/126 [=====================>........] - ETA: 0s - loss: 1.4056e-04 - mean_absolute_error: 0.0095118/126 [===========================>..] - ETA: 0s - loss: 1.3398e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 3ms/step - loss: 1.3127e-04 - mean_absolute_error: 0.0091 - val_loss: 1.0302e-04 - val_mean_absolute_error: 0.0078
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 1.2048e-04 - mean_absolute_error: 0.0091 25/126 [====>.........................] - ETA: 0s - loss: 1.0308e-04 - mean_absolute_error: 0.0079 48/126 [==========>...................] - ETA: 0s - loss: 1.0282e-04 - mean_absolute_error: 0.0078 72/126 [================>.............] - ETA: 0s - loss: 1.0397e-04 - mean_absolute_error: 0.0078 96/126 [=====================>........] - ETA: 0s - loss: 1.0075e-04 - mean_absolute_error: 0.0077119/126 [===========================>..] - ETA: 0s - loss: 1.0178e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0202e-04 - mean_absolute_error: 0.0077 - val_loss: 1.3408e-04 - val_mean_absolute_error: 0.0093
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 6.9718e-05 - mean_absolute_error: 0.0062 25/126 [====>.........................] - ETA: 0s - loss: 9.1791e-05 - mean_absolute_error: 0.0074 47/126 [==========>...................] - ETA: 0s - loss: 9.1973e-05 - mean_absolute_error: 0.0075 73/126 [================>.............] - ETA: 0s - loss: 9.5117e-05 - mean_absolute_error: 0.0075 99/126 [======================>.......] - ETA: 0s - loss: 9.4269e-05 - mean_absolute_error: 0.0074123/126 [============================>.] - ETA: 0s - loss: 9.7022e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.6435e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1465e-04 - val_mean_absolute_error: 0.0084
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 7.5300e-05 - mean_absolute_error: 0.0071 25/126 [====>.........................] - ETA: 0s - loss: 1.0454e-04 - mean_absolute_error: 0.0076 49/126 [==========>...................] - ETA: 0s - loss: 9.9674e-05 - mean_absolute_error: 0.0075 71/126 [===============>..............] - ETA: 0s - loss: 9.6526e-05 - mean_absolute_error: 0.0073 95/126 [=====================>........] - ETA: 0s - loss: 9.6469e-05 - mean_absolute_error: 0.0074118/126 [===========================>..] - ETA: 0s - loss: 9.5205e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 3ms/step - loss: 9.6687e-05 - mean_absolute_error: 0.0074 - val_loss: 1.0942e-04 - val_mean_absolute_error: 0.0082
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 7.8333e-05 - mean_absolute_error: 0.0074 24/126 [====>.........................] - ETA: 0s - loss: 1.0049e-04 - mean_absolute_error: 0.0074 50/126 [==========>...................] - ETA: 0s - loss: 1.0160e-04 - mean_absolute_error: 0.0075 76/126 [=================>............] - ETA: 0s - loss: 9.9806e-05 - mean_absolute_error: 0.0075103/126 [=======================>......] - ETA: 0s - loss: 9.9041e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 9.8934e-05 - mean_absolute_error: 0.0076 - val_loss: 1.0565e-04 - val_mean_absolute_error: 0.0080
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 1.9085e-04 - mean_absolute_error: 0.0093 22/126 [====>.........................] - ETA: 0s - loss: 9.1363e-05 - mean_absolute_error: 0.0073 48/126 [==========>...................] - ETA: 0s - loss: 9.3282e-05 - mean_absolute_error: 0.0073 71/126 [===============>..............] - ETA: 0s - loss: 9.7740e-05 - mean_absolute_error: 0.0074 97/126 [======================>.......] - ETA: 0s - loss: 9.9733e-05 - mean_absolute_error: 0.0075123/126 [============================>.] - ETA: 0s - loss: 9.7381e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7310e-05 - mean_absolute_error: 0.0075 - val_loss: 1.0509e-04 - val_mean_absolute_error: 0.0080
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 1.2548e-04 - mean_absolute_error: 0.0088 23/126 [====>.........................] - ETA: 0s - loss: 9.4016e-05 - mean_absolute_error: 0.0074 48/126 [==========>...................] - ETA: 0s - loss: 1.0077e-04 - mean_absolute_error: 0.0076 72/126 [================>.............] - ETA: 0s - loss: 9.8575e-05 - mean_absolute_error: 0.0076 98/126 [======================>.......] - ETA: 0s - loss: 9.6929e-05 - mean_absolute_error: 0.0075119/126 [===========================>..] - ETA: 0s - loss: 9.8413e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7405e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1962e-04 - val_mean_absolute_error: 0.0087
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 6.5185e-05 - mean_absolute_error: 0.0058 24/126 [====>.........................] - ETA: 0s - loss: 8.7705e-05 - mean_absolute_error: 0.0073 50/126 [==========>...................] - ETA: 0s - loss: 9.2967e-05 - mean_absolute_error: 0.0073 75/126 [================>.............] - ETA: 0s - loss: 9.4607e-05 - mean_absolute_error: 0.0074101/126 [=======================>......] - ETA: 0s - loss: 9.3144e-05 - mean_absolute_error: 0.0073126/126 [==============================] - ETA: 0s - loss: 9.7184e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.7184e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1719e-04 - val_mean_absolute_error: 0.0086
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 8.2881e-05 - mean_absolute_error: 0.0077 23/126 [====>.........................] - ETA: 0s - loss: 8.1968e-05 - mean_absolute_error: 0.0068 49/126 [==========>...................] - ETA: 0s - loss: 8.7964e-05 - mean_absolute_error: 0.0071 75/126 [================>.............] - ETA: 0s - loss: 9.5656e-05 - mean_absolute_error: 0.0074100/126 [======================>.......] - ETA: 0s - loss: 9.6549e-05 - mean_absolute_error: 0.0074124/126 [============================>.] - ETA: 0s - loss: 9.6742e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.6215e-05 - mean_absolute_error: 0.0074 - val_loss: 1.1084e-04 - val_mean_absolute_error: 0.0082
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 1.0416e-04 - mean_absolute_error: 0.0075 23/126 [====>.........................] - ETA: 0s - loss: 9.9358e-05 - mean_absolute_error: 0.0075 49/126 [==========>...................] - ETA: 0s - loss: 8.9865e-05 - mean_absolute_error: 0.0072 75/126 [================>.............] - ETA: 0s - loss: 9.5612e-05 - mean_absolute_error: 0.0075100/126 [======================>.......] - ETA: 0s - loss: 9.7161e-05 - mean_absolute_error: 0.0075125/126 [============================>.] - ETA: 0s - loss: 1.0252e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0236e-04 - mean_absolute_error: 0.0077 - val_loss: 1.2355e-04 - val_mean_absolute_error: 0.0089
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 1.2479e-04 - mean_absolute_error: 0.0079 19/126 [===>..........................] - ETA: 0s - loss: 1.0076e-04 - mean_absolute_error: 0.0075 41/126 [========>.....................] - ETA: 0s - loss: 9.8988e-05 - mean_absolute_error: 0.0075 66/126 [==============>...............] - ETA: 0s - loss: 9.5836e-05 - mean_absolute_error: 0.0074 91/126 [====================>.........] - ETA: 0s - loss: 9.8012e-05 - mean_absolute_error: 0.0075117/126 [==========================>...] - ETA: 0s - loss: 9.9841e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 3ms/step - loss: 1.0012e-04 - mean_absolute_error: 0.0075 - val_loss: 1.3938e-04 - val_mean_absolute_error: 0.0095
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 1.3525e-04 - mean_absolute_error: 0.0094 19/126 [===>..........................] - ETA: 0s - loss: 9.9887e-05 - mean_absolute_error: 0.0077 43/126 [=========>....................] - ETA: 0s - loss: 1.0500e-04 - mean_absolute_error: 0.0076 69/126 [===============>..............] - ETA: 0s - loss: 1.0264e-04 - mean_absolute_error: 0.0076 94/126 [=====================>........] - ETA: 0s - loss: 1.0042e-04 - mean_absolute_error: 0.0076119/126 [===========================>..] - ETA: 0s - loss: 9.7248e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 3ms/step - loss: 9.8535e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1864e-04 - val_mean_absolute_error: 0.0086
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 1.4481e-04 - mean_absolute_error: 0.0075 21/126 [====>.........................] - ETA: 0s - loss: 1.0580e-04 - mean_absolute_error: 0.0077 46/126 [=========>....................] - ETA: 0s - loss: 1.0785e-04 - mean_absolute_error: 0.0078 73/126 [================>.............] - ETA: 0s - loss: 1.0331e-04 - mean_absolute_error: 0.0077 98/126 [======================>.......] - ETA: 0s - loss: 1.0234e-04 - mean_absolute_error: 0.0076124/126 [============================>.] - ETA: 0s - loss: 1.0208e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0333e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0540e-04 - val_mean_absolute_error: 0.0080
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 1.3335e-04 - mean_absolute_error: 0.0097 23/126 [====>.........................] - ETA: 0s - loss: 1.0077e-04 - mean_absolute_error: 0.0076 46/126 [=========>....................] - ETA: 0s - loss: 1.1563e-04 - mean_absolute_error: 0.0081 64/126 [==============>...............] - ETA: 0s - loss: 1.1424e-04 - mean_absolute_error: 0.0081 80/126 [==================>...........] - ETA: 0s - loss: 1.1639e-04 - mean_absolute_error: 0.0082 98/126 [======================>.......] - ETA: 0s - loss: 1.1209e-04 - mean_absolute_error: 0.0081111/126 [=========================>....] - ETA: 0s - loss: 1.0953e-04 - mean_absolute_error: 0.0080125/126 [============================>.] - ETA: 0s - loss: 1.0814e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 3ms/step - loss: 1.0814e-04 - mean_absolute_error: 0.0079 - val_loss: 1.1619e-04 - val_mean_absolute_error: 0.0085
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 5.9198e-05 - mean_absolute_error: 0.0057 22/126 [====>.........................] - ETA: 0s - loss: 9.1179e-05 - mean_absolute_error: 0.0073 45/126 [=========>....................] - ETA: 0s - loss: 9.6205e-05 - mean_absolute_error: 0.0074 67/126 [==============>...............] - ETA: 0s - loss: 9.2354e-05 - mean_absolute_error: 0.0073 89/126 [====================>.........] - ETA: 0s - loss: 9.8806e-05 - mean_absolute_error: 0.0076112/126 [=========================>....] - ETA: 0s - loss: 9.8674e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 3ms/step - loss: 1.0037e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0919e-04 - val_mean_absolute_error: 0.0081
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 9.7328e-05 - mean_absolute_error: 0.0075 25/126 [====>.........................] - ETA: 0s - loss: 1.2145e-04 - mean_absolute_error: 0.0084 49/126 [==========>...................] - ETA: 0s - loss: 1.0919e-04 - mean_absolute_error: 0.0079 73/126 [================>.............] - ETA: 0s - loss: 1.0795e-04 - mean_absolute_error: 0.0078 97/126 [======================>.......] - ETA: 0s - loss: 1.0569e-04 - mean_absolute_error: 0.0077120/126 [===========================>..] - ETA: 0s - loss: 1.0315e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 3ms/step - loss: 1.0256e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0509e-04 - val_mean_absolute_error: 0.0080
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 9.6787e-05 - mean_absolute_error: 0.0076 24/126 [====>.........................] - ETA: 0s - loss: 9.6329e-05 - mean_absolute_error: 0.0074 48/126 [==========>...................] - ETA: 0s - loss: 9.8267e-05 - mean_absolute_error: 0.0075 73/126 [================>.............] - ETA: 0s - loss: 1.0100e-04 - mean_absolute_error: 0.0076 97/126 [======================>.......] - ETA: 0s - loss: 9.9835e-05 - mean_absolute_error: 0.0076121/126 [===========================>..] - ETA: 0s - loss: 9.9008e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.8602e-05 - mean_absolute_error: 0.0075 - val_loss: 1.1862e-04 - val_mean_absolute_error: 0.0087
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 8.1378e-05 - mean_absolute_error: 0.0067 25/126 [====>.........................] - ETA: 0s - loss: 8.2019e-05 - mean_absolute_error: 0.0069 50/126 [==========>...................] - ETA: 0s - loss: 9.9280e-05 - mean_absolute_error: 0.0075 74/126 [================>.............] - ETA: 0s - loss: 1.0129e-04 - mean_absolute_error: 0.0076 98/126 [======================>.......] - ETA: 0s - loss: 1.0062e-04 - mean_absolute_error: 0.0076120/126 [===========================>..] - ETA: 0s - loss: 1.0120e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0075e-04 - mean_absolute_error: 0.0076 - val_loss: 1.3195e-04 - val_mean_absolute_error: 0.0092
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 9.3499e-05 - mean_absolute_error: 0.0077 25/126 [====>.........................] - ETA: 0s - loss: 9.7184e-05 - mean_absolute_error: 0.0075 50/126 [==========>...................] - ETA: 0s - loss: 9.6932e-05 - mean_absolute_error: 0.0076 75/126 [================>.............] - ETA: 0s - loss: 9.9127e-05 - mean_absolute_error: 0.0076 99/126 [======================>.......] - ETA: 0s - loss: 1.0405e-04 - mean_absolute_error: 0.0078123/126 [============================>.] - ETA: 0s - loss: 1.0190e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0161e-04 - mean_absolute_error: 0.0076 - val_loss: 1.1087e-04 - val_mean_absolute_error: 0.0083
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 5.5480e-05 - mean_absolute_error: 0.0061 23/126 [====>.........................] - ETA: 0s - loss: 8.7872e-05 - mean_absolute_error: 0.0073 48/126 [==========>...................] - ETA: 0s - loss: 9.3414e-05 - mean_absolute_error: 0.0073 72/126 [================>.............] - ETA: 0s - loss: 9.5633e-05 - mean_absolute_error: 0.0074 96/126 [=====================>........] - ETA: 0s - loss: 9.7927e-05 - mean_absolute_error: 0.0075118/126 [===========================>..] - ETA: 0s - loss: 9.9678e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 3ms/step - loss: 1.0003e-04 - mean_absolute_error: 0.0075 - val_loss: 1.2565e-04 - val_mean_absolute_error: 0.0090
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 7.9450e-05 - mean_absolute_error: 0.0074 25/126 [====>.........................] - ETA: 0s - loss: 9.8912e-05 - mean_absolute_error: 0.0075 50/126 [==========>...................] - ETA: 0s - loss: 1.0336e-04 - mean_absolute_error: 0.0076 74/126 [================>.............] - ETA: 0s - loss: 9.8794e-05 - mean_absolute_error: 0.0075 99/126 [======================>.......] - ETA: 0s - loss: 9.7475e-05 - mean_absolute_error: 0.0075123/126 [============================>.] - ETA: 0s - loss: 9.9559e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.9412e-05 - mean_absolute_error: 0.0075 - val_loss: 1.2989e-04 - val_mean_absolute_error: 0.0091
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 7.4199e-05 - mean_absolute_error: 0.0071 26/126 [=====>........................] - ETA: 0s - loss: 9.2947e-05 - mean_absolute_error: 0.0076 51/126 [===========>..................] - ETA: 0s - loss: 9.5316e-05 - mean_absolute_error: 0.0074 75/126 [================>.............] - ETA: 0s - loss: 9.4714e-05 - mean_absolute_error: 0.0074100/126 [======================>.......] - ETA: 0s - loss: 9.4934e-05 - mean_absolute_error: 0.0074124/126 [============================>.] - ETA: 0s - loss: 9.7676e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.7950e-05 - mean_absolute_error: 0.0075 - val_loss: 1.6811e-04 - val_mean_absolute_error: 0.0106
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 2.6050e-04 - mean_absolute_error: 0.0105 25/126 [====>.........................] - ETA: 0s - loss: 1.1278e-04 - mean_absolute_error: 0.0083 46/126 [=========>....................] - ETA: 0s - loss: 1.0497e-04 - mean_absolute_error: 0.0079 69/126 [===============>..............] - ETA: 0s - loss: 1.0159e-04 - mean_absolute_error: 0.0077 94/126 [=====================>........] - ETA: 0s - loss: 1.0501e-04 - mean_absolute_error: 0.0078118/126 [===========================>..] - ETA: 0s - loss: 1.0481e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 3ms/step - loss: 1.0309e-04 - mean_absolute_error: 0.0077 - val_loss: 1.1406e-04 - val_mean_absolute_error: 0.0083
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 1.0045e-04 - mean_absolute_error: 0.0080 25/126 [====>.........................] - ETA: 0s - loss: 1.1964e-04 - mean_absolute_error: 0.0085 49/126 [==========>...................] - ETA: 0s - loss: 1.1576e-04 - mean_absolute_error: 0.0084 74/126 [================>.............] - ETA: 0s - loss: 1.1784e-04 - mean_absolute_error: 0.0084 99/126 [======================>.......] - ETA: 0s - loss: 1.1929e-04 - mean_absolute_error: 0.0085123/126 [============================>.] - ETA: 0s - loss: 1.1538e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 2ms/step - loss: 1.1473e-04 - mean_absolute_error: 0.0083 - val_loss: 1.2552e-04 - val_mean_absolute_error: 0.0088
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 1.4166e-04 - mean_absolute_error: 0.0085 26/126 [=====>........................] - ETA: 0s - loss: 1.0724e-04 - mean_absolute_error: 0.0079 51/126 [===========>..................] - ETA: 0s - loss: 1.1532e-04 - mean_absolute_error: 0.0083 76/126 [=================>............] - ETA: 0s - loss: 1.0951e-04 - mean_absolute_error: 0.0081101/126 [=======================>......] - ETA: 0s - loss: 1.1194e-04 - mean_absolute_error: 0.0081125/126 [============================>.] - ETA: 0s - loss: 1.0935e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0934e-04 - mean_absolute_error: 0.0080 - val_loss: 1.0887e-04 - val_mean_absolute_error: 0.0082
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 6.6513e-05 - mean_absolute_error: 0.0068 24/126 [====>.........................] - ETA: 0s - loss: 9.0232e-05 - mean_absolute_error: 0.0074 48/126 [==========>...................] - ETA: 0s - loss: 9.9905e-05 - mean_absolute_error: 0.0076 70/126 [===============>..............] - ETA: 0s - loss: 1.0275e-04 - mean_absolute_error: 0.0076 94/126 [=====================>........] - ETA: 0s - loss: 1.0563e-04 - mean_absolute_error: 0.0078118/126 [===========================>..] - ETA: 0s - loss: 1.0516e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 3ms/step - loss: 1.0444e-04 - mean_absolute_error: 0.0077 - val_loss: 1.0405e-04 - val_mean_absolute_error: 0.0079
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 1.1595e-04 - mean_absolute_error: 0.0085 23/126 [====>.........................] - ETA: 0s - loss: 9.3938e-05 - mean_absolute_error: 0.0071 47/126 [==========>...................] - ETA: 0s - loss: 9.5075e-05 - mean_absolute_error: 0.0073 71/126 [===============>..............] - ETA: 0s - loss: 9.4148e-05 - mean_absolute_error: 0.0073 95/126 [=====================>........] - ETA: 0s - loss: 1.0063e-04 - mean_absolute_error: 0.0076119/126 [===========================>..] - ETA: 0s - loss: 1.0164e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0158e-04 - mean_absolute_error: 0.0076 - val_loss: 1.0929e-04 - val_mean_absolute_error: 0.0082
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 8.8461e-05 - mean_absolute_error: 0.0061 25/126 [====>.........................] - ETA: 0s - loss: 1.0732e-04 - mean_absolute_error: 0.0079 50/126 [==========>...................] - ETA: 0s - loss: 1.0275e-04 - mean_absolute_error: 0.0077 74/126 [================>.............] - ETA: 0s - loss: 1.0001e-04 - mean_absolute_error: 0.0076 99/126 [======================>.......] - ETA: 0s - loss: 1.0871e-04 - mean_absolute_error: 0.0079124/126 [============================>.] - ETA: 0s - loss: 1.1566e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 2ms/step - loss: 1.1524e-04 - mean_absolute_error: 0.0082 - val_loss: 1.5633e-04 - val_mean_absolute_error: 0.0099
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 1.3658e-04 - mean_absolute_error: 0.0094 25/126 [====>.........................] - ETA: 0s - loss: 1.0487e-04 - mean_absolute_error: 0.0078 50/126 [==========>...................] - ETA: 0s - loss: 1.0631e-04 - mean_absolute_error: 0.0079 74/126 [================>.............] - ETA: 0s - loss: 1.1791e-04 - mean_absolute_error: 0.0083 96/126 [=====================>........] - ETA: 0s - loss: 1.1767e-04 - mean_absolute_error: 0.0083119/126 [===========================>..] - ETA: 0s - loss: 1.1231e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 3ms/step - loss: 1.1093e-04 - mean_absolute_error: 0.0081 - val_loss: 1.0478e-04 - val_mean_absolute_error: 0.0080
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 2.2430e-04 - mean_absolute_error: 0.0096 25/126 [====>.........................] - ETA: 0s - loss: 9.7874e-05 - mean_absolute_error: 0.0074 47/126 [==========>...................] - ETA: 0s - loss: 9.6413e-05 - mean_absolute_error: 0.0074 71/126 [===============>..............] - ETA: 0s - loss: 9.7211e-05 - mean_absolute_error: 0.0075 95/126 [=====================>........] - ETA: 0s - loss: 9.8772e-05 - mean_absolute_error: 0.0075117/126 [==========================>...] - ETA: 0s - loss: 9.7839e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 3ms/step - loss: 9.9212e-05 - mean_absolute_error: 0.0076 - val_loss: 1.2823e-04 - val_mean_absolute_error: 0.0089
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 2.3395e-04 - mean_absolute_error: 0.0113 25/126 [====>.........................] - ETA: 0s - loss: 9.9498e-05 - mean_absolute_error: 0.0075 49/126 [==========>...................] - ETA: 0s - loss: 1.0259e-04 - mean_absolute_error: 0.0077 74/126 [================>.............] - ETA: 0s - loss: 1.0688e-04 - mean_absolute_error: 0.0079 99/126 [======================>.......] - ETA: 0s - loss: 1.1727e-04 - mean_absolute_error: 0.0084123/126 [============================>.] - ETA: 0s - loss: 1.2272e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 2ms/step - loss: 1.2228e-04 - mean_absolute_error: 0.0085 - val_loss: 1.0344e-04 - val_mean_absolute_error: 0.0079
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 8.0691e-05 - mean_absolute_error: 0.0069 25/126 [====>.........................] - ETA: 0s - loss: 1.0665e-04 - mean_absolute_error: 0.0079 50/126 [==========>...................] - ETA: 0s - loss: 1.0978e-04 - mean_absolute_error: 0.0079 75/126 [================>.............] - ETA: 0s - loss: 1.0849e-04 - mean_absolute_error: 0.0079 99/126 [======================>.......] - ETA: 0s - loss: 1.1608e-04 - mean_absolute_error: 0.0083121/126 [===========================>..] - ETA: 0s - loss: 1.1482e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1367e-04 - mean_absolute_error: 0.0082 - val_loss: 1.1330e-04 - val_mean_absolute_error: 0.0084
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 7.4781e-05 - mean_absolute_error: 0.0072 25/126 [====>.........................] - ETA: 0s - loss: 9.0102e-05 - mean_absolute_error: 0.0074 47/126 [==========>...................] - ETA: 0s - loss: 9.2186e-05 - mean_absolute_error: 0.0074 66/126 [==============>...............] - ETA: 0s - loss: 1.1265e-04 - mean_absolute_error: 0.0082 89/126 [====================>.........] - ETA: 0s - loss: 1.1114e-04 - mean_absolute_error: 0.0081113/126 [=========================>....] - ETA: 0s - loss: 1.1676e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 3ms/step - loss: 1.1252e-04 - mean_absolute_error: 0.0082 - val_loss: 1.3377e-04 - val_mean_absolute_error: 0.0093
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 1.0278e-04 - mean_absolute_error: 0.0076 25/126 [====>.........................] - ETA: 0s - loss: 9.3595e-05 - mean_absolute_error: 0.0074 50/126 [==========>...................] - ETA: 0s - loss: 9.7080e-05 - mean_absolute_error: 0.0076 75/126 [================>.............] - ETA: 0s - loss: 1.0200e-04 - mean_absolute_error: 0.0077 99/126 [======================>.......] - ETA: 0s - loss: 1.0259e-04 - mean_absolute_error: 0.0076124/126 [============================>.] - ETA: 0s - loss: 1.0101e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 1.0083e-04 - mean_absolute_error: 0.0076 - val_loss: 1.1947e-04 - val_mean_absolute_error: 0.0085
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 1.1380e-04 - mean_absolute_error: 0.0092 26/126 [=====>........................] - ETA: 0s - loss: 1.2835e-04 - mean_absolute_error: 0.0086 51/126 [===========>..................] - ETA: 0s - loss: 1.2359e-04 - mean_absolute_error: 0.0085 75/126 [================>.............] - ETA: 0s - loss: 1.1635e-04 - mean_absolute_error: 0.0083 99/126 [======================>.......] - ETA: 0s - loss: 1.1080e-04 - mean_absolute_error: 0.0081124/126 [============================>.] - ETA: 0s - loss: 1.0768e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0769e-04 - mean_absolute_error: 0.0079 - val_loss: 1.1610e-04 - val_mean_absolute_error: 0.0086
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 9.0524e-05 - mean_absolute_error: 0.0074 24/126 [====>.........................] - ETA: 0s - loss: 9.4571e-05 - mean_absolute_error: 0.0074 48/126 [==========>...................] - ETA: 0s - loss: 1.2086e-04 - mean_absolute_error: 0.0084 72/126 [================>.............] - ETA: 0s - loss: 1.1942e-04 - mean_absolute_error: 0.0084 94/126 [=====================>........] - ETA: 0s - loss: 1.1800e-04 - mean_absolute_error: 0.0084119/126 [===========================>..] - ETA: 0s - loss: 1.2133e-04 - mean_absolute_error: 0.0085126/126 [==============================] - 0s 3ms/step - loss: 1.2525e-04 - mean_absolute_error: 0.0087 - val_loss: 2.6324e-04 - val_mean_absolute_error: 0.0136
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 2.1113e-04 - mean_absolute_error: 0.0127 25/126 [====>.........................] - ETA: 0s - loss: 1.1051e-04 - mean_absolute_error: 0.0079 49/126 [==========>...................] - ETA: 0s - loss: 1.0419e-04 - mean_absolute_error: 0.0077 73/126 [================>.............] - ETA: 0s - loss: 1.0627e-04 - mean_absolute_error: 0.0078 98/126 [======================>.......] - ETA: 0s - loss: 1.1207e-04 - mean_absolute_error: 0.0081123/126 [============================>.] - ETA: 0s - loss: 1.1264e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.1296e-04 - mean_absolute_error: 0.0081 - val_loss: 1.1776e-04 - val_mean_absolute_error: 0.0085
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 2.2003e-04 - mean_absolute_error: 0.0099 26/126 [=====>........................] - ETA: 0s - loss: 9.8209e-05 - mean_absolute_error: 0.0075 51/126 [===========>..................] - ETA: 0s - loss: 1.0056e-04 - mean_absolute_error: 0.0076 76/126 [=================>............] - ETA: 0s - loss: 1.0666e-04 - mean_absolute_error: 0.0079101/126 [=======================>......] - ETA: 0s - loss: 1.0597e-04 - mean_absolute_error: 0.0078126/126 [==============================] - ETA: 0s - loss: 1.0432e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0432e-04 - mean_absolute_error: 0.0078 - val_loss: 1.3048e-04 - val_mean_absolute_error: 0.0092
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 7.4684e-05 - mean_absolute_error: 0.0067 23/126 [====>.........................] - ETA: 0s - loss: 1.3937e-04 - mean_absolute_error: 0.0094 46/126 [=========>....................] - ETA: 0s - loss: 1.4573e-04 - mean_absolute_error: 0.0096 70/126 [===============>..............] - ETA: 0s - loss: 1.3777e-04 - mean_absolute_error: 0.0092 94/126 [=====================>........] - ETA: 0s - loss: 1.3523e-04 - mean_absolute_error: 0.0091117/126 [==========================>...] - ETA: 0s - loss: 1.2805e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 3ms/step - loss: 1.2721e-04 - mean_absolute_error: 0.0088 - val_loss: 1.1463e-04 - val_mean_absolute_error: 0.0084
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 9.7076e-05 - mean_absolute_error: 0.0076 25/126 [====>.........................] - ETA: 0s - loss: 1.3145e-04 - mean_absolute_error: 0.0087 49/126 [==========>...................] - ETA: 0s - loss: 1.1587e-04 - mean_absolute_error: 0.0083 74/126 [================>.............] - ETA: 0s - loss: 1.1500e-04 - mean_absolute_error: 0.0082 99/126 [======================>.......] - ETA: 0s - loss: 1.1182e-04 - mean_absolute_error: 0.0081123/126 [============================>.] - ETA: 0s - loss: 1.1415e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1397e-04 - mean_absolute_error: 0.0083 - val_loss: 1.4207e-04 - val_mean_absolute_error: 0.0096
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 8.5496e-05 - mean_absolute_error: 0.0073 25/126 [====>.........................] - ETA: 0s - loss: 1.5676e-04 - mean_absolute_error: 0.0100 50/126 [==========>...................] - ETA: 0s - loss: 1.2671e-04 - mean_absolute_error: 0.0088 75/126 [================>.............] - ETA: 0s - loss: 1.1772e-04 - mean_absolute_error: 0.0084100/126 [======================>.......] - ETA: 0s - loss: 1.1380e-04 - mean_absolute_error: 0.0082125/126 [============================>.] - ETA: 0s - loss: 1.1145e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.1133e-04 - mean_absolute_error: 0.0081 - val_loss: 1.0720e-04 - val_mean_absolute_error: 0.0081
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 1.1460e-04 - mean_absolute_error: 0.0090 25/126 [====>.........................] - ETA: 0s - loss: 1.2938e-04 - mean_absolute_error: 0.0090 50/126 [==========>...................] - ETA: 0s - loss: 1.3415e-04 - mean_absolute_error: 0.0090 72/126 [================>.............] - ETA: 0s - loss: 1.2314e-04 - mean_absolute_error: 0.0085 95/126 [=====================>........] - ETA: 0s - loss: 1.1726e-04 - mean_absolute_error: 0.0083118/126 [===========================>..] - ETA: 0s - loss: 1.1700e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 3ms/step - loss: 1.1812e-04 - mean_absolute_error: 0.0084 - val_loss: 1.4136e-04 - val_mean_absolute_error: 0.0094
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 2.1109e-04 - mean_absolute_error: 0.0117 24/126 [====>.........................] - ETA: 0s - loss: 1.7886e-04 - mean_absolute_error: 0.0107 49/126 [==========>...................] - ETA: 0s - loss: 1.6161e-04 - mean_absolute_error: 0.0101 73/126 [================>.............] - ETA: 0s - loss: 1.4368e-04 - mean_absolute_error: 0.0093 98/126 [======================>.......] - ETA: 0s - loss: 1.3151e-04 - mean_absolute_error: 0.0088123/126 [============================>.] - ETA: 0s - loss: 1.2901e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.2872e-04 - mean_absolute_error: 0.0088 - val_loss: 1.4691e-04 - val_mean_absolute_error: 0.0096
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 2.4527e-04 - mean_absolute_error: 0.0127 26/126 [=====>........................] - ETA: 0s - loss: 1.0534e-04 - mean_absolute_error: 0.0078 51/126 [===========>..................] - ETA: 0s - loss: 9.7350e-05 - mean_absolute_error: 0.0075 76/126 [=================>............] - ETA: 0s - loss: 1.0186e-04 - mean_absolute_error: 0.0076100/126 [======================>.......] - ETA: 0s - loss: 9.9879e-05 - mean_absolute_error: 0.0076125/126 [============================>.] - ETA: 0s - loss: 1.0340e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 2ms/step - loss: 1.0363e-04 - mean_absolute_error: 0.0077 - val_loss: 1.9339e-04 - val_mean_absolute_error: 0.0113
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 1.8436e-04 - mean_absolute_error: 0.0112 26/126 [=====>........................] - ETA: 0s - loss: 1.0683e-04 - mean_absolute_error: 0.0079 39/126 [========>.....................] - ETA: 0s - loss: 1.1207e-04 - mean_absolute_error: 0.0081 54/126 [===========>..................] - ETA: 0s - loss: 1.1198e-04 - mean_absolute_error: 0.0080 78/126 [=================>............] - ETA: 0s - loss: 1.1431e-04 - mean_absolute_error: 0.0082103/126 [=======================>......] - ETA: 0s - loss: 1.1171e-04 - mean_absolute_error: 0.0081126/126 [==============================] - ETA: 0s - loss: 1.1267e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 3ms/step - loss: 1.1267e-04 - mean_absolute_error: 0.0081 - val_loss: 1.1492e-04 - val_mean_absolute_error: 0.0085
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 6.8561e-05 - mean_absolute_error: 0.0066 25/126 [====>.........................] - ETA: 0s - loss: 9.5304e-05 - mean_absolute_error: 0.0075 50/126 [==========>...................] - ETA: 0s - loss: 1.0086e-04 - mean_absolute_error: 0.0077 75/126 [================>.............] - ETA: 0s - loss: 1.1184e-04 - mean_absolute_error: 0.0081100/126 [======================>.......] - ETA: 0s - loss: 1.1466e-04 - mean_absolute_error: 0.0083124/126 [============================>.] - ETA: 0s - loss: 1.1059e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.1027e-04 - mean_absolute_error: 0.0081 - val_loss: 1.0429e-04 - val_mean_absolute_error: 0.0079
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 1.0536e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.1586e-04 - mean_absolute_error: 0.0085 51/126 [===========>..................] - ETA: 0s - loss: 1.0404e-04 - mean_absolute_error: 0.0080 75/126 [================>.............] - ETA: 0s - loss: 9.9158e-05 - mean_absolute_error: 0.0077100/126 [======================>.......] - ETA: 0s - loss: 9.7589e-05 - mean_absolute_error: 0.0076124/126 [============================>.] - ETA: 0s - loss: 1.0246e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0239e-04 - mean_absolute_error: 0.0078 - val_loss: 1.1881e-04 - val_mean_absolute_error: 0.0087
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 1.0378e-04 - mean_absolute_error: 0.0079 25/126 [====>.........................] - ETA: 0s - loss: 1.0602e-04 - mean_absolute_error: 0.0079 50/126 [==========>...................] - ETA: 0s - loss: 1.1536e-04 - mean_absolute_error: 0.0083 72/126 [================>.............] - ETA: 0s - loss: 1.4609e-04 - mean_absolute_error: 0.0094 94/126 [=====================>........] - ETA: 0s - loss: 1.3656e-04 - mean_absolute_error: 0.0091118/126 [===========================>..] - ETA: 0s - loss: 1.2956e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.2637e-04 - mean_absolute_error: 0.0086 - val_loss: 1.2922e-04 - val_mean_absolute_error: 0.0090
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 1.5164e-04 - mean_absolute_error: 0.0094 22/126 [====>.........................] - ETA: 0s - loss: 1.3198e-04 - mean_absolute_error: 0.0090 47/126 [==========>...................] - ETA: 0s - loss: 1.5205e-04 - mean_absolute_error: 0.0097 72/126 [================>.............] - ETA: 0s - loss: 1.3287e-04 - mean_absolute_error: 0.0089 97/126 [======================>.......] - ETA: 0s - loss: 1.2351e-04 - mean_absolute_error: 0.0086122/126 [============================>.] - ETA: 0s - loss: 1.1860e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.1779e-04 - mean_absolute_error: 0.0083 - val_loss: 1.0232e-04 - val_mean_absolute_error: 0.0078
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 3.0377e-05 - mean_absolute_error: 0.0042 25/126 [====>.........................] - ETA: 0s - loss: 1.0455e-04 - mean_absolute_error: 0.0076 50/126 [==========>...................] - ETA: 0s - loss: 1.1394e-04 - mean_absolute_error: 0.0080 74/126 [================>.............] - ETA: 0s - loss: 1.1299e-04 - mean_absolute_error: 0.0081 98/126 [======================>.......] - ETA: 0s - loss: 1.1496e-04 - mean_absolute_error: 0.0082122/126 [============================>.] - ETA: 0s - loss: 1.1020e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 2ms/step - loss: 1.0996e-04 - mean_absolute_error: 0.0081 - val_loss: 9.9054e-05 - val_mean_absolute_error: 0.0076
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 8.9029e-05 - mean_absolute_error: 0.0073 25/126 [====>.........................] - ETA: 0s - loss: 9.0689e-05 - mean_absolute_error: 0.0073 50/126 [==========>...................] - ETA: 0s - loss: 9.5605e-05 - mean_absolute_error: 0.0074 75/126 [================>.............] - ETA: 0s - loss: 9.7132e-05 - mean_absolute_error: 0.0075 97/126 [======================>.......] - ETA: 0s - loss: 9.8941e-05 - mean_absolute_error: 0.0076119/126 [===========================>..] - ETA: 0s - loss: 9.9547e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 9.8559e-05 - mean_absolute_error: 0.0076 - val_loss: 1.1711e-04 - val_mean_absolute_error: 0.0085
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 9.2462e-05 - mean_absolute_error: 0.0079 26/126 [=====>........................] - ETA: 0s - loss: 1.1282e-04 - mean_absolute_error: 0.0081 48/126 [==========>...................] - ETA: 0s - loss: 1.1672e-04 - mean_absolute_error: 0.0083 72/126 [================>.............] - ETA: 0s - loss: 1.0672e-04 - mean_absolute_error: 0.0078 96/126 [=====================>........] - ETA: 0s - loss: 1.0938e-04 - mean_absolute_error: 0.0081120/126 [===========================>..] - ETA: 0s - loss: 1.1424e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 3ms/step - loss: 1.1478e-04 - mean_absolute_error: 0.0083 - val_loss: 2.4540e-04 - val_mean_absolute_error: 0.0131
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 2.2124e-04 - mean_absolute_error: 0.0121 25/126 [====>.........................] - ETA: 0s - loss: 1.5822e-04 - mean_absolute_error: 0.0101 50/126 [==========>...................] - ETA: 0s - loss: 1.3121e-04 - mean_absolute_error: 0.0090 74/126 [================>.............] - ETA: 0s - loss: 1.2564e-04 - mean_absolute_error: 0.0087 99/126 [======================>.......] - ETA: 0s - loss: 1.2432e-04 - mean_absolute_error: 0.0087123/126 [============================>.] - ETA: 0s - loss: 1.2369e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 2ms/step - loss: 1.2430e-04 - mean_absolute_error: 0.0087 - val_loss: 1.6701e-04 - val_mean_absolute_error: 0.0106
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 8.2033e-05 - mean_absolute_error: 0.0074 25/126 [====>.........................] - ETA: 0s - loss: 1.5400e-04 - mean_absolute_error: 0.0096 49/126 [==========>...................] - ETA: 0s - loss: 1.2575e-04 - mean_absolute_error: 0.0086 73/126 [================>.............] - ETA: 0s - loss: 1.1399e-04 - mean_absolute_error: 0.0081 97/126 [======================>.......] - ETA: 0s - loss: 1.0751e-04 - mean_absolute_error: 0.0079120/126 [===========================>..] - ETA: 0s - loss: 1.0696e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0608e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0251e-04 - val_mean_absolute_error: 0.0079
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 8.7767e-05 - mean_absolute_error: 0.0067 24/126 [====>.........................] - ETA: 0s - loss: 8.7781e-05 - mean_absolute_error: 0.0070 48/126 [==========>...................] - ETA: 0s - loss: 8.6171e-05 - mean_absolute_error: 0.0071 70/126 [===============>..............] - ETA: 0s - loss: 9.4153e-05 - mean_absolute_error: 0.0074 94/126 [=====================>........] - ETA: 0s - loss: 9.4728e-05 - mean_absolute_error: 0.0074118/126 [===========================>..] - ETA: 0s - loss: 1.0465e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 3ms/step - loss: 1.0582e-04 - mean_absolute_error: 0.0079 - val_loss: 9.8130e-05 - val_mean_absolute_error: 0.0077
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 9.1672e-05 - mean_absolute_error: 0.0072 26/126 [=====>........................] - ETA: 0s - loss: 9.9625e-05 - mean_absolute_error: 0.0078 51/126 [===========>..................] - ETA: 0s - loss: 1.5097e-04 - mean_absolute_error: 0.0095 75/126 [================>.............] - ETA: 0s - loss: 1.5032e-04 - mean_absolute_error: 0.0096 99/126 [======================>.......] - ETA: 0s - loss: 1.3744e-04 - mean_absolute_error: 0.0091124/126 [============================>.] - ETA: 0s - loss: 1.3041e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.3055e-04 - mean_absolute_error: 0.0088 - val_loss: 1.5691e-04 - val_mean_absolute_error: 0.0100
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 1.3741e-04 - mean_absolute_error: 0.0101 26/126 [=====>........................] - ETA: 0s - loss: 1.2205e-04 - mean_absolute_error: 0.0087 51/126 [===========>..................] - ETA: 0s - loss: 1.1983e-04 - mean_absolute_error: 0.0086 76/126 [=================>............] - ETA: 0s - loss: 1.2115e-04 - mean_absolute_error: 0.0087100/126 [======================>.......] - ETA: 0s - loss: 1.2934e-04 - mean_absolute_error: 0.0089125/126 [============================>.] - ETA: 0s - loss: 1.3308e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.3311e-04 - mean_absolute_error: 0.0090 - val_loss: 1.0435e-04 - val_mean_absolute_error: 0.0080
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 6.7437e-05 - mean_absolute_error: 0.0062 24/126 [====>.........................] - ETA: 0s - loss: 1.1712e-04 - mean_absolute_error: 0.0080 48/126 [==========>...................] - ETA: 0s - loss: 1.0196e-04 - mean_absolute_error: 0.0076 72/126 [================>.............] - ETA: 0s - loss: 9.9481e-05 - mean_absolute_error: 0.0075 95/126 [=====================>........] - ETA: 0s - loss: 9.8431e-05 - mean_absolute_error: 0.0075118/126 [===========================>..] - ETA: 0s - loss: 1.0226e-04 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 3ms/step - loss: 1.0292e-04 - mean_absolute_error: 0.0077 - val_loss: 9.7899e-05 - val_mean_absolute_error: 0.0076
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 9.0160e-05 - mean_absolute_error: 0.0080 26/126 [=====>........................] - ETA: 0s - loss: 8.8214e-05 - mean_absolute_error: 0.0072 51/126 [===========>..................] - ETA: 0s - loss: 9.5916e-05 - mean_absolute_error: 0.0075 75/126 [================>.............] - ETA: 0s - loss: 1.0655e-04 - mean_absolute_error: 0.0078100/126 [======================>.......] - ETA: 0s - loss: 1.1439e-04 - mean_absolute_error: 0.0082125/126 [============================>.] - ETA: 0s - loss: 1.1049e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.1046e-04 - mean_absolute_error: 0.0080 - val_loss: 9.6681e-05 - val_mean_absolute_error: 0.0075
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 7.2602e-05 - mean_absolute_error: 0.0065 26/126 [=====>........................] - ETA: 0s - loss: 9.9353e-05 - mean_absolute_error: 0.0074 51/126 [===========>..................] - ETA: 0s - loss: 9.7491e-05 - mean_absolute_error: 0.0075 75/126 [================>.............] - ETA: 0s - loss: 1.0086e-04 - mean_absolute_error: 0.0076 99/126 [======================>.......] - ETA: 0s - loss: 1.0704e-04 - mean_absolute_error: 0.0080123/126 [============================>.] - ETA: 0s - loss: 1.1277e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1255e-04 - mean_absolute_error: 0.0082 - val_loss: 1.4139e-04 - val_mean_absolute_error: 0.0094
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 1.1290e-04 - mean_absolute_error: 0.0087 26/126 [=====>........................] - ETA: 0s - loss: 1.0430e-04 - mean_absolute_error: 0.0080 48/126 [==========>...................] - ETA: 0s - loss: 1.1442e-04 - mean_absolute_error: 0.0083 70/126 [===============>..............] - ETA: 0s - loss: 1.1172e-04 - mean_absolute_error: 0.0082 94/126 [=====================>........] - ETA: 0s - loss: 1.1583e-04 - mean_absolute_error: 0.0083117/126 [==========================>...] - ETA: 0s - loss: 1.1108e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 3ms/step - loss: 1.0954e-04 - mean_absolute_error: 0.0081 - val_loss: 1.0138e-04 - val_mean_absolute_error: 0.0079
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 8.4637e-05 - mean_absolute_error: 0.0078 25/126 [====>.........................] - ETA: 0s - loss: 1.0563e-04 - mean_absolute_error: 0.0079 49/126 [==========>...................] - ETA: 0s - loss: 1.0491e-04 - mean_absolute_error: 0.0079 73/126 [================>.............] - ETA: 0s - loss: 1.1246e-04 - mean_absolute_error: 0.0081 97/126 [======================>.......] - ETA: 0s - loss: 1.1213e-04 - mean_absolute_error: 0.0081121/126 [===========================>..] - ETA: 0s - loss: 1.0825e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0842e-04 - mean_absolute_error: 0.0080 - val_loss: 9.6694e-05 - val_mean_absolute_error: 0.0076
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 6.2493e-05 - mean_absolute_error: 0.0067 25/126 [====>.........................] - ETA: 0s - loss: 9.4253e-05 - mean_absolute_error: 0.0075 50/126 [==========>...................] - ETA: 0s - loss: 1.0131e-04 - mean_absolute_error: 0.0076 75/126 [================>.............] - ETA: 0s - loss: 1.1317e-04 - mean_absolute_error: 0.0081 99/126 [======================>.......] - ETA: 0s - loss: 1.1854e-04 - mean_absolute_error: 0.0084123/126 [============================>.] - ETA: 0s - loss: 1.1273e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1192e-04 - mean_absolute_error: 0.0081 - val_loss: 9.6736e-05 - val_mean_absolute_error: 0.0077
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 1.3640e-04 - mean_absolute_error: 0.0074 26/126 [=====>........................] - ETA: 0s - loss: 1.0188e-04 - mean_absolute_error: 0.0075 51/126 [===========>..................] - ETA: 0s - loss: 1.0377e-04 - mean_absolute_error: 0.0078 73/126 [================>.............] - ETA: 0s - loss: 1.0768e-04 - mean_absolute_error: 0.0080 98/126 [======================>.......] - ETA: 0s - loss: 1.1022e-04 - mean_absolute_error: 0.0081122/126 [============================>.] - ETA: 0s - loss: 1.0858e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0826e-04 - mean_absolute_error: 0.0080 - val_loss: 1.2363e-04 - val_mean_absolute_error: 0.0088
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 1.2578e-04 - mean_absolute_error: 0.0092 22/126 [====>.........................] - ETA: 0s - loss: 1.0875e-04 - mean_absolute_error: 0.0080 46/126 [=========>....................] - ETA: 0s - loss: 1.0097e-04 - mean_absolute_error: 0.0077 70/126 [===============>..............] - ETA: 0s - loss: 1.0782e-04 - mean_absolute_error: 0.0079 89/126 [====================>.........] - ETA: 0s - loss: 1.0245e-04 - mean_absolute_error: 0.0077108/126 [========================>.....] - ETA: 0s - loss: 9.9589e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 3ms/step - loss: 9.8965e-05 - mean_absolute_error: 0.0076 - val_loss: 9.6762e-05 - val_mean_absolute_error: 0.0076
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 4.7592e-05 - mean_absolute_error: 0.0057 26/126 [=====>........................] - ETA: 0s - loss: 9.3197e-05 - mean_absolute_error: 0.0074 51/126 [===========>..................] - ETA: 0s - loss: 1.0351e-04 - mean_absolute_error: 0.0078 76/126 [=================>............] - ETA: 0s - loss: 1.0110e-04 - mean_absolute_error: 0.0077101/126 [=======================>......] - ETA: 0s - loss: 9.9660e-05 - mean_absolute_error: 0.0076125/126 [============================>.] - ETA: 0s - loss: 1.0571e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0555e-04 - mean_absolute_error: 0.0079 - val_loss: 1.7641e-04 - val_mean_absolute_error: 0.0110
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 1.9343e-04 - mean_absolute_error: 0.0115 26/126 [=====>........................] - ETA: 0s - loss: 1.1653e-04 - mean_absolute_error: 0.0081 51/126 [===========>..................] - ETA: 0s - loss: 1.1275e-04 - mean_absolute_error: 0.0081 75/126 [================>.............] - ETA: 0s - loss: 1.1174e-04 - mean_absolute_error: 0.0081 97/126 [======================>.......] - ETA: 0s - loss: 1.0737e-04 - mean_absolute_error: 0.0079119/126 [===========================>..] - ETA: 0s - loss: 1.0718e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 3ms/step - loss: 1.0672e-04 - mean_absolute_error: 0.0079 - val_loss: 9.4655e-05 - val_mean_absolute_error: 0.0075
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 7.0995e-05 - mean_absolute_error: 0.0067 22/126 [====>.........................] - ETA: 0s - loss: 1.0386e-04 - mean_absolute_error: 0.0080 46/126 [=========>....................] - ETA: 0s - loss: 1.0064e-04 - mean_absolute_error: 0.0077 71/126 [===============>..............] - ETA: 0s - loss: 9.9452e-05 - mean_absolute_error: 0.0076 96/126 [=====================>........] - ETA: 0s - loss: 9.9314e-05 - mean_absolute_error: 0.0075120/126 [===========================>..] - ETA: 0s - loss: 1.0739e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0816e-04 - mean_absolute_error: 0.0080 - val_loss: 1.1212e-04 - val_mean_absolute_error: 0.0083
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 1.7776e-04 - mean_absolute_error: 0.0099 26/126 [=====>........................] - ETA: 0s - loss: 9.2611e-05 - mean_absolute_error: 0.0076 51/126 [===========>..................] - ETA: 0s - loss: 9.9838e-05 - mean_absolute_error: 0.0076 76/126 [=================>............] - ETA: 0s - loss: 9.8220e-05 - mean_absolute_error: 0.0076101/126 [=======================>......] - ETA: 0s - loss: 1.0262e-04 - mean_absolute_error: 0.0078126/126 [==============================] - ETA: 0s - loss: 1.0729e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0729e-04 - mean_absolute_error: 0.0080 - val_loss: 3.2172e-04 - val_mean_absolute_error: 0.0155
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 3.7054e-04 - mean_absolute_error: 0.0166 25/126 [====>.........................] - ETA: 0s - loss: 1.5660e-04 - mean_absolute_error: 0.0097 50/126 [==========>...................] - ETA: 0s - loss: 1.2390e-04 - mean_absolute_error: 0.0086 75/126 [================>.............] - ETA: 0s - loss: 1.2224e-04 - mean_absolute_error: 0.0085 99/126 [======================>.......] - ETA: 0s - loss: 1.2353e-04 - mean_absolute_error: 0.0086121/126 [===========================>..] - ETA: 0s - loss: 1.2037e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.1915e-04 - mean_absolute_error: 0.0084 - val_loss: 9.3876e-05 - val_mean_absolute_error: 0.0074
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 6.6221e-05 - mean_absolute_error: 0.0063 23/126 [====>.........................] - ETA: 0s - loss: 9.3507e-05 - mean_absolute_error: 0.0074 46/126 [=========>....................] - ETA: 0s - loss: 9.1870e-05 - mean_absolute_error: 0.0072 68/126 [===============>..............] - ETA: 0s - loss: 9.3054e-05 - mean_absolute_error: 0.0073 92/126 [====================>.........] - ETA: 0s - loss: 9.6988e-05 - mean_absolute_error: 0.0074116/126 [==========================>...] - ETA: 0s - loss: 9.4235e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 3ms/step - loss: 9.9706e-05 - mean_absolute_error: 0.0076 - val_loss: 2.1107e-04 - val_mean_absolute_error: 0.0120
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 1.3491e-04 - mean_absolute_error: 0.0102 25/126 [====>.........................] - ETA: 0s - loss: 1.2101e-04 - mean_absolute_error: 0.0087 49/126 [==========>...................] - ETA: 0s - loss: 1.0910e-04 - mean_absolute_error: 0.0081 74/126 [================>.............] - ETA: 0s - loss: 1.0320e-04 - mean_absolute_error: 0.0078 99/126 [======================>.......] - ETA: 0s - loss: 1.0294e-04 - mean_absolute_error: 0.0078123/126 [============================>.] - ETA: 0s - loss: 1.0466e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0437e-04 - mean_absolute_error: 0.0078 - val_loss: 1.1639e-04 - val_mean_absolute_error: 0.0086
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 5.8111e-05 - mean_absolute_error: 0.0066 25/126 [====>.........................] - ETA: 0s - loss: 1.3849e-04 - mean_absolute_error: 0.0092 50/126 [==========>...................] - ETA: 0s - loss: 1.3745e-04 - mean_absolute_error: 0.0092 75/126 [================>.............] - ETA: 0s - loss: 1.2280e-04 - mean_absolute_error: 0.0086 99/126 [======================>.......] - ETA: 0s - loss: 1.1468e-04 - mean_absolute_error: 0.0083123/126 [============================>.] - ETA: 0s - loss: 1.0964e-04 - mean_absolute_error: 0.0080126/126 [==============================] - 0s 2ms/step - loss: 1.0947e-04 - mean_absolute_error: 0.0080 - val_loss: 1.1839e-04 - val_mean_absolute_error: 0.0087
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 1.1032e-04 - mean_absolute_error: 0.0087 24/126 [====>.........................] - ETA: 0s - loss: 1.0760e-04 - mean_absolute_error: 0.0076 48/126 [==========>...................] - ETA: 0s - loss: 9.8923e-05 - mean_absolute_error: 0.0074 72/126 [================>.............] - ETA: 0s - loss: 9.6214e-05 - mean_absolute_error: 0.0074 94/126 [=====================>........] - ETA: 0s - loss: 1.0077e-04 - mean_absolute_error: 0.0077119/126 [===========================>..] - ETA: 0s - loss: 9.7196e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.6100e-05 - mean_absolute_error: 0.0075 - val_loss: 9.3228e-05 - val_mean_absolute_error: 0.0073
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 6.4699e-05 - mean_absolute_error: 0.0063 23/126 [====>.........................] - ETA: 0s - loss: 1.0013e-04 - mean_absolute_error: 0.0075 46/126 [=========>....................] - ETA: 0s - loss: 9.1414e-05 - mean_absolute_error: 0.0073 70/126 [===============>..............] - ETA: 0s - loss: 9.0141e-05 - mean_absolute_error: 0.0073 94/126 [=====================>........] - ETA: 0s - loss: 8.9587e-05 - mean_absolute_error: 0.0073118/126 [===========================>..] - ETA: 0s - loss: 1.0089e-04 - mean_absolute_error: 0.0077126/126 [==============================] - 0s 3ms/step - loss: 1.0007e-04 - mean_absolute_error: 0.0077 - val_loss: 1.6590e-04 - val_mean_absolute_error: 0.0106
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 1.0868e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.0400e-04 - mean_absolute_error: 0.0077 50/126 [==========>...................] - ETA: 0s - loss: 1.0439e-04 - mean_absolute_error: 0.0078 74/126 [================>.............] - ETA: 0s - loss: 9.7915e-05 - mean_absolute_error: 0.0076 99/126 [======================>.......] - ETA: 0s - loss: 9.2992e-05 - mean_absolute_error: 0.0074124/126 [============================>.] - ETA: 0s - loss: 9.5883e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.5862e-05 - mean_absolute_error: 0.0075 - val_loss: 9.2997e-05 - val_mean_absolute_error: 0.0074
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 8.8061e-05 - mean_absolute_error: 0.0071 23/126 [====>.........................] - ETA: 0s - loss: 1.2240e-04 - mean_absolute_error: 0.0088 46/126 [=========>....................] - ETA: 0s - loss: 1.1269e-04 - mean_absolute_error: 0.0083 70/126 [===============>..............] - ETA: 0s - loss: 1.0624e-04 - mean_absolute_error: 0.0080 94/126 [=====================>........] - ETA: 0s - loss: 1.0372e-04 - mean_absolute_error: 0.0078117/126 [==========================>...] - ETA: 0s - loss: 1.0425e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 3ms/step - loss: 1.0306e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0416e-04 - val_mean_absolute_error: 0.0080
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 1.1667e-04 - mean_absolute_error: 0.0089 25/126 [====>.........................] - ETA: 0s - loss: 1.1712e-04 - mean_absolute_error: 0.0083 50/126 [==========>...................] - ETA: 0s - loss: 1.0936e-04 - mean_absolute_error: 0.0081 74/126 [================>.............] - ETA: 0s - loss: 1.0510e-04 - mean_absolute_error: 0.0079 98/126 [======================>.......] - ETA: 0s - loss: 9.9438e-05 - mean_absolute_error: 0.0076122/126 [============================>.] - ETA: 0s - loss: 9.9159e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 2ms/step - loss: 9.8681e-05 - mean_absolute_error: 0.0076 - val_loss: 9.2125e-05 - val_mean_absolute_error: 0.0074
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 8.0882e-05 - mean_absolute_error: 0.0064 26/126 [=====>........................] - ETA: 0s - loss: 1.1246e-04 - mean_absolute_error: 0.0081 51/126 [===========>..................] - ETA: 0s - loss: 1.0259e-04 - mean_absolute_error: 0.0078 76/126 [=================>............] - ETA: 0s - loss: 1.1065e-04 - mean_absolute_error: 0.0081100/126 [======================>.......] - ETA: 0s - loss: 1.0312e-04 - mean_absolute_error: 0.0078124/126 [============================>.] - ETA: 0s - loss: 1.0355e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0405e-04 - mean_absolute_error: 0.0078 - val_loss: 1.5059e-04 - val_mean_absolute_error: 0.0098
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 1.1673e-04 - mean_absolute_error: 0.0093 26/126 [=====>........................] - ETA: 0s - loss: 1.5853e-04 - mean_absolute_error: 0.0100 48/126 [==========>...................] - ETA: 0s - loss: 1.2698e-04 - mean_absolute_error: 0.0086 71/126 [===============>..............] - ETA: 0s - loss: 1.1118e-04 - mean_absolute_error: 0.0080 94/126 [=====================>........] - ETA: 0s - loss: 1.0735e-04 - mean_absolute_error: 0.0079119/126 [===========================>..] - ETA: 0s - loss: 1.0448e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 3ms/step - loss: 1.0439e-04 - mean_absolute_error: 0.0078 - val_loss: 1.0112e-04 - val_mean_absolute_error: 0.0078
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 6.8708e-05 - mean_absolute_error: 0.0069 24/126 [====>.........................] - ETA: 0s - loss: 9.3811e-05 - mean_absolute_error: 0.0074 48/126 [==========>...................] - ETA: 0s - loss: 8.9684e-05 - mean_absolute_error: 0.0072 72/126 [================>.............] - ETA: 0s - loss: 9.3824e-05 - mean_absolute_error: 0.0074 96/126 [=====================>........] - ETA: 0s - loss: 9.3300e-05 - mean_absolute_error: 0.0074120/126 [===========================>..] - ETA: 0s - loss: 9.4121e-05 - mean_absolute_error: 0.0074126/126 [==============================] - 0s 2ms/step - loss: 9.4664e-05 - mean_absolute_error: 0.0074 - val_loss: 2.9720e-04 - val_mean_absolute_error: 0.0151
Epoch 86/100
  1/126 [..............................] - ETA: 0s - loss: 4.0395e-04 - mean_absolute_error: 0.0158 25/126 [====>.........................] - ETA: 0s - loss: 1.2141e-04 - mean_absolute_error: 0.0083 50/126 [==========>...................] - ETA: 0s - loss: 1.2735e-04 - mean_absolute_error: 0.0087 75/126 [================>.............] - ETA: 0s - loss: 1.2995e-04 - mean_absolute_error: 0.0088 99/126 [======================>.......] - ETA: 0s - loss: 1.2086e-04 - mean_absolute_error: 0.0085123/126 [============================>.] - ETA: 0s - loss: 1.1477e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 2ms/step - loss: 1.1449e-04 - mean_absolute_error: 0.0083 - val_loss: 2.1176e-04 - val_mean_absolute_error: 0.0120
Epoch 87/100
  1/126 [..............................] - ETA: 0s - loss: 1.9242e-04 - mean_absolute_error: 0.0120 26/126 [=====>........................] - ETA: 0s - loss: 9.9936e-05 - mean_absolute_error: 0.0077 52/126 [===========>..................] - ETA: 0s - loss: 9.3597e-05 - mean_absolute_error: 0.0075 74/126 [================>.............] - ETA: 0s - loss: 9.4074e-05 - mean_absolute_error: 0.0075 99/126 [======================>.......] - ETA: 0s - loss: 9.1792e-05 - mean_absolute_error: 0.0074123/126 [============================>.] - ETA: 0s - loss: 9.1322e-05 - mean_absolute_error: 0.0073126/126 [==============================] - 0s 2ms/step - loss: 9.1520e-05 - mean_absolute_error: 0.0073 - val_loss: 2.7234e-04 - val_mean_absolute_error: 0.0143
Epoch 88/100
  1/126 [..............................] - ETA: 0s - loss: 1.8224e-04 - mean_absolute_error: 0.0118 22/126 [====>.........................] - ETA: 0s - loss: 1.0176e-04 - mean_absolute_error: 0.0078 47/126 [==========>...................] - ETA: 0s - loss: 9.5239e-05 - mean_absolute_error: 0.0074 72/126 [================>.............] - ETA: 0s - loss: 1.1353e-04 - mean_absolute_error: 0.0082 97/126 [======================>.......] - ETA: 0s - loss: 1.1341e-04 - mean_absolute_error: 0.0081122/126 [============================>.] - ETA: 0s - loss: 1.1184e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1205e-04 - mean_absolute_error: 0.0082 - val_loss: 1.5740e-04 - val_mean_absolute_error: 0.0103
Epoch 89/100
  1/126 [..............................] - ETA: 0s - loss: 1.1237e-04 - mean_absolute_error: 0.0080 25/126 [====>.........................] - ETA: 0s - loss: 8.9849e-05 - mean_absolute_error: 0.0072 50/126 [==========>...................] - ETA: 0s - loss: 1.0968e-04 - mean_absolute_error: 0.0080 74/126 [================>.............] - ETA: 0s - loss: 1.1300e-04 - mean_absolute_error: 0.0082 99/126 [======================>.......] - ETA: 0s - loss: 1.0698e-04 - mean_absolute_error: 0.0079123/126 [============================>.] - ETA: 0s - loss: 1.0604e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0658e-04 - mean_absolute_error: 0.0079 - val_loss: 2.5426e-04 - val_mean_absolute_error: 0.0138
Epoch 90/100
  1/126 [..............................] - ETA: 0s - loss: 2.8712e-04 - mean_absolute_error: 0.0140 25/126 [====>.........................] - ETA: 0s - loss: 1.7719e-04 - mean_absolute_error: 0.0104 50/126 [==========>...................] - ETA: 0s - loss: 1.3049e-04 - mean_absolute_error: 0.0086 76/126 [=================>............] - ETA: 0s - loss: 1.2467e-04 - mean_absolute_error: 0.0085 99/126 [======================>.......] - ETA: 0s - loss: 1.1782e-04 - mean_absolute_error: 0.0084122/126 [============================>.] - ETA: 0s - loss: 1.1201e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 2ms/step - loss: 1.1173e-04 - mean_absolute_error: 0.0082 - val_loss: 1.8516e-04 - val_mean_absolute_error: 0.0114
Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 1.1634e-04 - mean_absolute_error: 0.0093 23/126 [====>.........................] - ETA: 0s - loss: 1.0166e-04 - mean_absolute_error: 0.0079 47/126 [==========>...................] - ETA: 0s - loss: 1.1549e-04 - mean_absolute_error: 0.0084 69/126 [===============>..............] - ETA: 0s - loss: 1.2022e-04 - mean_absolute_error: 0.0085 93/126 [=====================>........] - ETA: 0s - loss: 1.0956e-04 - mean_absolute_error: 0.0081117/126 [==========================>...] - ETA: 0s - loss: 1.1307e-04 - mean_absolute_error: 0.0082126/126 [==============================] - 0s 3ms/step - loss: 1.1201e-04 - mean_absolute_error: 0.0082 - val_loss: 1.5096e-04 - val_mean_absolute_error: 0.0099
Epoch 92/100
  1/126 [..............................] - ETA: 0s - loss: 9.2777e-05 - mean_absolute_error: 0.0076 26/126 [=====>........................] - ETA: 0s - loss: 1.1601e-04 - mean_absolute_error: 0.0084 51/126 [===========>..................] - ETA: 0s - loss: 1.1049e-04 - mean_absolute_error: 0.0082 76/126 [=================>............] - ETA: 0s - loss: 1.1248e-04 - mean_absolute_error: 0.0083100/126 [======================>.......] - ETA: 0s - loss: 1.0337e-04 - mean_absolute_error: 0.0079124/126 [============================>.] - ETA: 0s - loss: 1.0193e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0197e-04 - mean_absolute_error: 0.0078 - val_loss: 1.3097e-04 - val_mean_absolute_error: 0.0091
Epoch 93/100
  1/126 [..............................] - ETA: 0s - loss: 1.1862e-04 - mean_absolute_error: 0.0094 26/126 [=====>........................] - ETA: 0s - loss: 9.3672e-05 - mean_absolute_error: 0.0075 51/126 [===========>..................] - ETA: 0s - loss: 1.0417e-04 - mean_absolute_error: 0.0078 76/126 [=================>............] - ETA: 0s - loss: 1.1191e-04 - mean_absolute_error: 0.0082101/126 [=======================>......] - ETA: 0s - loss: 1.1023e-04 - mean_absolute_error: 0.0081125/126 [============================>.] - ETA: 0s - loss: 1.0455e-04 - mean_absolute_error: 0.0079126/126 [==============================] - 0s 2ms/step - loss: 1.0449e-04 - mean_absolute_error: 0.0079 - val_loss: 1.2712e-04 - val_mean_absolute_error: 0.0089
Epoch 94/100
  1/126 [..............................] - ETA: 0s - loss: 7.8378e-05 - mean_absolute_error: 0.0072 26/126 [=====>........................] - ETA: 0s - loss: 8.4129e-05 - mean_absolute_error: 0.0069 45/126 [=========>....................] - ETA: 0s - loss: 8.4131e-05 - mean_absolute_error: 0.0069 66/126 [==============>...............] - ETA: 0s - loss: 9.6855e-05 - mean_absolute_error: 0.0075 87/126 [===================>..........] - ETA: 0s - loss: 9.9462e-05 - mean_absolute_error: 0.0076111/126 [=========================>....] - ETA: 0s - loss: 9.8555e-05 - mean_absolute_error: 0.0076126/126 [==============================] - 0s 3ms/step - loss: 9.7475e-05 - mean_absolute_error: 0.0076 - val_loss: 1.1119e-04 - val_mean_absolute_error: 0.0083
Epoch 95/100
  1/126 [..............................] - ETA: 0s - loss: 8.1469e-05 - mean_absolute_error: 0.0073 24/126 [====>.........................] - ETA: 0s - loss: 8.7535e-05 - mean_absolute_error: 0.0070 48/126 [==========>...................] - ETA: 0s - loss: 8.7213e-05 - mean_absolute_error: 0.0070 72/126 [================>.............] - ETA: 0s - loss: 8.4887e-05 - mean_absolute_error: 0.0069 95/126 [=====================>........] - ETA: 0s - loss: 8.6580e-05 - mean_absolute_error: 0.0070119/126 [===========================>..] - ETA: 0s - loss: 8.9859e-05 - mean_absolute_error: 0.0072126/126 [==============================] - 0s 3ms/step - loss: 8.9134e-05 - mean_absolute_error: 0.0072 - val_loss: 8.9703e-05 - val_mean_absolute_error: 0.0074
Epoch 96/100
  1/126 [..............................] - ETA: 0s - loss: 6.0128e-05 - mean_absolute_error: 0.0064 25/126 [====>.........................] - ETA: 0s - loss: 9.3741e-05 - mean_absolute_error: 0.0074 50/126 [==========>...................] - ETA: 0s - loss: 9.7435e-05 - mean_absolute_error: 0.0075 74/126 [================>.............] - ETA: 0s - loss: 9.7671e-05 - mean_absolute_error: 0.0076 99/126 [======================>.......] - ETA: 0s - loss: 9.6837e-05 - mean_absolute_error: 0.0076124/126 [============================>.] - ETA: 0s - loss: 9.6433e-05 - mean_absolute_error: 0.0075126/126 [==============================] - 0s 2ms/step - loss: 9.6229e-05 - mean_absolute_error: 0.0075 - val_loss: 9.4282e-05 - val_mean_absolute_error: 0.0076
Epoch 97/100
  1/126 [..............................] - ETA: 0s - loss: 5.8263e-05 - mean_absolute_error: 0.0057 24/126 [====>.........................] - ETA: 0s - loss: 8.5925e-05 - mean_absolute_error: 0.0072 47/126 [==========>...................] - ETA: 0s - loss: 1.0741e-04 - mean_absolute_error: 0.0082 71/126 [===============>..............] - ETA: 0s - loss: 1.0197e-04 - mean_absolute_error: 0.0079 93/126 [=====================>........] - ETA: 0s - loss: 1.0022e-04 - mean_absolute_error: 0.0078116/126 [==========================>...] - ETA: 0s - loss: 1.1506e-04 - mean_absolute_error: 0.0083126/126 [==============================] - 0s 3ms/step - loss: 1.1303e-04 - mean_absolute_error: 0.0082 - val_loss: 8.7667e-05 - val_mean_absolute_error: 0.0071
Epoch 98/100
  1/126 [..............................] - ETA: 0s - loss: 5.0474e-05 - mean_absolute_error: 0.0057 25/126 [====>.........................] - ETA: 0s - loss: 1.1097e-04 - mean_absolute_error: 0.0082 50/126 [==========>...................] - ETA: 0s - loss: 1.1401e-04 - mean_absolute_error: 0.0083 75/126 [================>.............] - ETA: 0s - loss: 1.2569e-04 - mean_absolute_error: 0.0087 99/126 [======================>.......] - ETA: 0s - loss: 1.2613e-04 - mean_absolute_error: 0.0088124/126 [============================>.] - ETA: 0s - loss: 1.1783e-04 - mean_absolute_error: 0.0084126/126 [==============================] - 0s 2ms/step - loss: 1.1722e-04 - mean_absolute_error: 0.0084 - val_loss: 1.1223e-04 - val_mean_absolute_error: 0.0085
Epoch 99/100
  1/126 [..............................] - ETA: 0s - loss: 1.1027e-04 - mean_absolute_error: 0.0063 25/126 [====>.........................] - ETA: 0s - loss: 9.7848e-05 - mean_absolute_error: 0.0075 50/126 [==========>...................] - ETA: 0s - loss: 9.7400e-05 - mean_absolute_error: 0.0073 75/126 [================>.............] - ETA: 0s - loss: 1.0378e-04 - mean_absolute_error: 0.0078 99/126 [======================>.......] - ETA: 0s - loss: 1.0560e-04 - mean_absolute_error: 0.0079124/126 [============================>.] - ETA: 0s - loss: 1.0232e-04 - mean_absolute_error: 0.0078126/126 [==============================] - 0s 2ms/step - loss: 1.0252e-04 - mean_absolute_error: 0.0078 - val_loss: 8.7353e-05 - val_mean_absolute_error: 0.0072
Epoch 100/100
  1/126 [..............................] - ETA: 0s - loss: 6.8393e-05 - mean_absolute_error: 0.0066 25/126 [====>.........................] - ETA: 0s - loss: 8.1685e-05 - mean_absolute_error: 0.0066 48/126 [==========>...................] - ETA: 0s - loss: 8.6991e-05 - mean_absolute_error: 0.0070 72/126 [================>.............] - ETA: 0s - loss: 1.0210e-04 - mean_absolute_error: 0.0076 96/126 [=====================>........] - ETA: 0s - loss: 1.1462e-04 - mean_absolute_error: 0.0082120/126 [===========================>..] - ETA: 0s - loss: 1.0941e-04 - mean_absolute_error: 0.0081126/126 [==============================] - 0s 3ms/step - loss: 1.1083e-04 - mean_absolute_error: 0.0081 - val_loss: 1.6799e-04 - val_mean_absolute_error: 0.0108

<keras.src.callbacks.History at 0x2115a855090>

After the training and fitting of the Machine Learning model for Canada, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Austrailia with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
canada_train_pred = canada_model.predict(X_canada_train).flatten()

plt.plot(dates_canada_train, canada_train_pred, linewidth=4)
plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Training Set Predictions vs. Observations")
plt.show()

canada_val_pred = canada_model.predict(X_canada_val).flatten()

plt.plot(dates_canada_val, canada_val_pred, linewidth=4)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Validation Set Predictions vs. Observations")
plt.show()

canada_test_pred = canada_model.predict(X_canada_test).flatten()

plt.plot(dates_canada_test, canada_test_pred, linewidth=4)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Canada - Testing Set Predictions vs. Observations")
plt.show()
```

  1/126 [..............................] - ETA: 40s 35/126 [=======>......................] - ETA: 0s  76/126 [=================>............] - ETA: 0s122/126 [============================>.] - ETA: 0s126/126 [==============================] - 0s 1ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 2ms/step
 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 2ms/step

Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Canada’s international currency rate with the United States.

```{python}
# Plotting Canada's observational (reference) data with the predictions of its 
# Machine Learning Model (as a way to visually inspect the effectiveness of the 
# model) 
plt.plot(dates_canada_train, canada_train_pred, linewidth=4)
plt.plot(dates_canada_train, y_canada_train, linewidth=1)
plt.plot(dates_canada_val, canada_val_pred, linewidth=4)
plt.plot(dates_canada_val, y_canada_val, linewidth=1)
plt.plot(dates_canada_test, canada_test_pred, linewidth=4)
plt.plot(dates_canada_test, y_canada_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"], loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations")
plt.show()
```

Then, I worked on the United Kingdom’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split the United Kingdom’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting United Kingdom's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_united_kingdom_train, X_united_kingdom_train, y_united_kingdom_train = dates_united_kingdom[:percentile_80], X_united_kingdom[:percentile_80], y_united_kingdom[:percentile_80]
dates_united_kingdom_val, X_united_kingdom_val, y_united_kingdom_val = dates_united_kingdom[percentile_80:percentile_90], X_united_kingdom[percentile_80:percentile_90], y_united_kingdom[percentile_80:percentile_90]
dates_united_kingdom_test, X_united_kingdom_test, y_united_kingdom_test = dates_united_kingdom[percentile_90:], X_united_kingdom[percentile_90:], y_united_kingdom[percentile_90:]

plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of United Kingdom's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for United Kingdom
united_kingdom_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

united_kingdom_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

united_kingdom_model.fit(X_united_kingdom_train, y_united_kingdom_train, validation_data=(X_united_kingdom_val, y_united_kingdom_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:41 - loss: 0.4255 - mean_absolute_error: 0.6487 20/126 [===>..........................] - ETA: 0s - loss: 0.2700 - mean_absolute_error: 0.5120   45/126 [=========>....................] - ETA: 0s - loss: 0.1427 - mean_absolute_error: 0.3202 70/126 [===============>..............] - ETA: 0s - loss: 0.0926 - mean_absolute_error: 0.2199 95/126 [=====================>........] - ETA: 0s - loss: 0.0684 - mean_absolute_error: 0.1684122/126 [============================>.] - ETA: 0s - loss: 0.0535 - mean_absolute_error: 0.1361126/126 [==============================] - 2s 6ms/step - loss: 0.0520 - mean_absolute_error: 0.1331 - val_loss: 0.0048 - val_mean_absolute_error: 0.0668
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 6.8098e-04 - mean_absolute_error: 0.0213 26/126 [=====>........................] - ETA: 0s - loss: 7.6637e-04 - mean_absolute_error: 0.0226 52/126 [===========>..................] - ETA: 0s - loss: 7.3276e-04 - mean_absolute_error: 0.0219 75/126 [================>.............] - ETA: 0s - loss: 7.0707e-04 - mean_absolute_error: 0.0214102/126 [=======================>......] - ETA: 0s - loss: 6.9295e-04 - mean_absolute_error: 0.0214126/126 [==============================] - 0s 2ms/step - loss: 6.8410e-04 - mean_absolute_error: 0.0213 - val_loss: 0.0035 - val_mean_absolute_error: 0.0562
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 6.3600e-04 - mean_absolute_error: 0.0201 22/126 [====>.........................] - ETA: 0s - loss: 5.8275e-04 - mean_absolute_error: 0.0195 49/126 [==========>...................] - ETA: 0s - loss: 5.8675e-04 - mean_absolute_error: 0.0196 75/126 [================>.............] - ETA: 0s - loss: 5.6806e-04 - mean_absolute_error: 0.0193100/126 [======================>.......] - ETA: 0s - loss: 5.5582e-04 - mean_absolute_error: 0.0192126/126 [==============================] - 0s 2ms/step - loss: 5.3206e-04 - mean_absolute_error: 0.0187 - val_loss: 0.0027 - val_mean_absolute_error: 0.0498
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 3.5350e-04 - mean_absolute_error: 0.0159 20/126 [===>..........................] - ETA: 0s - loss: 4.0822e-04 - mean_absolute_error: 0.0163 42/126 [=========>....................] - ETA: 0s - loss: 4.2554e-04 - mean_absolute_error: 0.0169 66/126 [==============>...............] - ETA: 0s - loss: 4.0742e-04 - mean_absolute_error: 0.0165 93/126 [=====================>........] - ETA: 0s - loss: 3.9439e-04 - mean_absolute_error: 0.0161118/126 [===========================>..] - ETA: 0s - loss: 3.8052e-04 - mean_absolute_error: 0.0158126/126 [==============================] - 0s 3ms/step - loss: 3.7358e-04 - mean_absolute_error: 0.0157 - val_loss: 0.0013 - val_mean_absolute_error: 0.0336
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 2.7776e-04 - mean_absolute_error: 0.0132 26/126 [=====>........................] - ETA: 0s - loss: 2.9362e-04 - mean_absolute_error: 0.0137 53/126 [===========>..................] - ETA: 0s - loss: 2.7146e-04 - mean_absolute_error: 0.0133 80/126 [==================>...........] - ETA: 0s - loss: 2.5078e-04 - mean_absolute_error: 0.0127103/126 [=======================>......] - ETA: 0s - loss: 2.4117e-04 - mean_absolute_error: 0.0125126/126 [==============================] - 0s 2ms/step - loss: 2.3251e-04 - mean_absolute_error: 0.0122 - val_loss: 6.3826e-04 - val_mean_absolute_error: 0.0233
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 1.5053e-04 - mean_absolute_error: 0.0097 24/126 [====>.........................] - ETA: 0s - loss: 1.4972e-04 - mean_absolute_error: 0.0099 51/126 [===========>..................] - ETA: 0s - loss: 1.4755e-04 - mean_absolute_error: 0.0097 76/126 [=================>............] - ETA: 0s - loss: 1.3784e-04 - mean_absolute_error: 0.0094103/126 [=======================>......] - ETA: 0s - loss: 1.3081e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.2500e-04 - mean_absolute_error: 0.0089 - val_loss: 4.0130e-04 - val_mean_absolute_error: 0.0183
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 1.2523e-04 - mean_absolute_error: 0.0092 19/126 [===>..........................] - ETA: 0s - loss: 9.0084e-05 - mean_absolute_error: 0.0075 45/126 [=========>....................] - ETA: 0s - loss: 8.1340e-05 - mean_absolute_error: 0.0071 72/126 [================>.............] - ETA: 0s - loss: 7.5726e-05 - mean_absolute_error: 0.0068 99/126 [======================>.......] - ETA: 0s - loss: 6.9926e-05 - mean_absolute_error: 0.0065126/126 [==============================] - ETA: 0s - loss: 6.6970e-05 - mean_absolute_error: 0.0064126/126 [==============================] - 0s 2ms/step - loss: 6.6970e-05 - mean_absolute_error: 0.0064 - val_loss: 7.3518e-05 - val_mean_absolute_error: 0.0062
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 4.8888e-05 - mean_absolute_error: 0.0054 23/126 [====>.........................] - ETA: 0s - loss: 4.8156e-05 - mean_absolute_error: 0.0054 44/126 [=========>....................] - ETA: 0s - loss: 4.9212e-05 - mean_absolute_error: 0.0054 68/126 [===============>..............] - ETA: 0s - loss: 4.8279e-05 - mean_absolute_error: 0.0053 94/126 [=====================>........] - ETA: 0s - loss: 4.5640e-05 - mean_absolute_error: 0.0052121/126 [===========================>..] - ETA: 0s - loss: 4.4212e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 3ms/step - loss: 4.4122e-05 - mean_absolute_error: 0.0050 - val_loss: 7.5121e-05 - val_mean_absolute_error: 0.0062
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 6.7339e-05 - mean_absolute_error: 0.0061 18/126 [===>..........................] - ETA: 0s - loss: 3.6810e-05 - mean_absolute_error: 0.0045 41/126 [========>.....................] - ETA: 0s - loss: 3.6106e-05 - mean_absolute_error: 0.0045 67/126 [==============>...............] - ETA: 0s - loss: 3.5036e-05 - mean_absolute_error: 0.0044 93/126 [=====================>........] - ETA: 0s - loss: 3.6646e-05 - mean_absolute_error: 0.0044118/126 [===========================>..] - ETA: 0s - loss: 3.5754e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.6499e-05 - mean_absolute_error: 0.0044 - val_loss: 7.3111e-05 - val_mean_absolute_error: 0.0061
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 2.9760e-05 - mean_absolute_error: 0.0042 20/126 [===>..........................] - ETA: 0s - loss: 3.7304e-05 - mean_absolute_error: 0.0045 44/126 [=========>....................] - ETA: 0s - loss: 3.6673e-05 - mean_absolute_error: 0.0044 71/126 [===============>..............] - ETA: 0s - loss: 3.6424e-05 - mean_absolute_error: 0.0044 98/126 [======================>.......] - ETA: 0s - loss: 3.5378e-05 - mean_absolute_error: 0.0044124/126 [============================>.] - ETA: 0s - loss: 3.5821e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5800e-05 - mean_absolute_error: 0.0043 - val_loss: 8.0403e-05 - val_mean_absolute_error: 0.0066
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 4.0608e-05 - mean_absolute_error: 0.0042 23/126 [====>.........................] - ETA: 0s - loss: 3.3387e-05 - mean_absolute_error: 0.0042 50/126 [==========>...................] - ETA: 0s - loss: 3.5453e-05 - mean_absolute_error: 0.0043 72/126 [================>.............] - ETA: 0s - loss: 3.4880e-05 - mean_absolute_error: 0.0043 99/126 [======================>.......] - ETA: 0s - loss: 3.5250e-05 - mean_absolute_error: 0.0043126/126 [==============================] - ETA: 0s - loss: 3.5914e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5914e-05 - mean_absolute_error: 0.0043 - val_loss: 1.1360e-04 - val_mean_absolute_error: 0.0085
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 3.6928e-05 - mean_absolute_error: 0.0047 23/126 [====>.........................] - ETA: 0s - loss: 3.3736e-05 - mean_absolute_error: 0.0044 48/126 [==========>...................] - ETA: 0s - loss: 3.5336e-05 - mean_absolute_error: 0.0044 75/126 [================>.............] - ETA: 0s - loss: 3.4194e-05 - mean_absolute_error: 0.0043101/126 [=======================>......] - ETA: 0s - loss: 3.5852e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5968e-05 - mean_absolute_error: 0.0043 - val_loss: 1.0113e-04 - val_mean_absolute_error: 0.0079
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 3.4341e-05 - mean_absolute_error: 0.0038 22/126 [====>.........................] - ETA: 0s - loss: 3.2261e-05 - mean_absolute_error: 0.0041 48/126 [==========>...................] - ETA: 0s - loss: 3.3271e-05 - mean_absolute_error: 0.0042 74/126 [================>.............] - ETA: 0s - loss: 3.5120e-05 - mean_absolute_error: 0.0042100/126 [======================>.......] - ETA: 0s - loss: 3.4765e-05 - mean_absolute_error: 0.0042126/126 [==============================] - ETA: 0s - loss: 3.5129e-05 - mean_absolute_error: 0.0042126/126 [==============================] - 0s 2ms/step - loss: 3.5129e-05 - mean_absolute_error: 0.0042 - val_loss: 8.9002e-05 - val_mean_absolute_error: 0.0071
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 3.5923e-05 - mean_absolute_error: 0.0046 22/126 [====>.........................] - ETA: 0s - loss: 3.8841e-05 - mean_absolute_error: 0.0048 48/126 [==========>...................] - ETA: 0s - loss: 3.5704e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.5099e-05 - mean_absolute_error: 0.0044100/126 [======================>.......] - ETA: 0s - loss: 3.8066e-05 - mean_absolute_error: 0.0046123/126 [============================>.] - ETA: 0s - loss: 3.8128e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7953e-05 - mean_absolute_error: 0.0045 - val_loss: 7.7813e-05 - val_mean_absolute_error: 0.0065
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 4.0964e-05 - mean_absolute_error: 0.0043 25/126 [====>.........................] - ETA: 0s - loss: 4.3418e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 4.1289e-05 - mean_absolute_error: 0.0047 76/126 [=================>............] - ETA: 0s - loss: 4.0628e-05 - mean_absolute_error: 0.0047100/126 [======================>.......] - ETA: 0s - loss: 3.8799e-05 - mean_absolute_error: 0.0046126/126 [==============================] - ETA: 0s - loss: 3.8405e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8405e-05 - mean_absolute_error: 0.0046 - val_loss: 9.1806e-05 - val_mean_absolute_error: 0.0073
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 3.3305e-05 - mean_absolute_error: 0.0041 23/126 [====>.........................] - ETA: 0s - loss: 3.7565e-05 - mean_absolute_error: 0.0046 50/126 [==========>...................] - ETA: 0s - loss: 3.6290e-05 - mean_absolute_error: 0.0044 76/126 [=================>............] - ETA: 0s - loss: 3.5546e-05 - mean_absolute_error: 0.0044103/126 [=======================>......] - ETA: 0s - loss: 3.5019e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6155e-05 - mean_absolute_error: 0.0044 - val_loss: 8.1353e-05 - val_mean_absolute_error: 0.0067
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 2.9057e-05 - mean_absolute_error: 0.0040 26/126 [=====>........................] - ETA: 0s - loss: 3.5372e-05 - mean_absolute_error: 0.0043 52/126 [===========>..................] - ETA: 0s - loss: 3.7340e-05 - mean_absolute_error: 0.0044 76/126 [=================>............] - ETA: 0s - loss: 3.7667e-05 - mean_absolute_error: 0.0045103/126 [=======================>......] - ETA: 0s - loss: 3.6316e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.8381e-05 - mean_absolute_error: 0.0046 - val_loss: 1.1399e-04 - val_mean_absolute_error: 0.0085
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 4.0940e-05 - mean_absolute_error: 0.0048 25/126 [====>.........................] - ETA: 0s - loss: 3.6855e-05 - mean_absolute_error: 0.0044 51/126 [===========>..................] - ETA: 0s - loss: 3.5808e-05 - mean_absolute_error: 0.0042 77/126 [=================>............] - ETA: 0s - loss: 3.6008e-05 - mean_absolute_error: 0.0043102/126 [=======================>......] - ETA: 0s - loss: 3.5826e-05 - mean_absolute_error: 0.0043126/126 [==============================] - ETA: 0s - loss: 3.6065e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6065e-05 - mean_absolute_error: 0.0044 - val_loss: 1.0490e-04 - val_mean_absolute_error: 0.0081
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 2.5909e-05 - mean_absolute_error: 0.0039 25/126 [====>.........................] - ETA: 0s - loss: 3.9539e-05 - mean_absolute_error: 0.0048 50/126 [==========>...................] - ETA: 0s - loss: 3.7225e-05 - mean_absolute_error: 0.0045 75/126 [================>.............] - ETA: 0s - loss: 3.6796e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.6610e-05 - mean_absolute_error: 0.0044125/126 [============================>.] - ETA: 0s - loss: 3.8097e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8090e-05 - mean_absolute_error: 0.0046 - val_loss: 9.7733e-05 - val_mean_absolute_error: 0.0077
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 2.0787e-05 - mean_absolute_error: 0.0030 26/126 [=====>........................] - ETA: 0s - loss: 3.4165e-05 - mean_absolute_error: 0.0043 51/126 [===========>..................] - ETA: 0s - loss: 3.7873e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 3.9338e-05 - mean_absolute_error: 0.0046101/126 [=======================>......] - ETA: 0s - loss: 3.7806e-05 - mean_absolute_error: 0.0045126/126 [==============================] - ETA: 0s - loss: 3.7590e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.7590e-05 - mean_absolute_error: 0.0045 - val_loss: 9.3433e-05 - val_mean_absolute_error: 0.0074
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 3.3883e-05 - mean_absolute_error: 0.0037 26/126 [=====>........................] - ETA: 0s - loss: 4.2317e-05 - mean_absolute_error: 0.0048 49/126 [==========>...................] - ETA: 0s - loss: 3.7050e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.8524e-05 - mean_absolute_error: 0.0046 97/126 [======================>.......] - ETA: 0s - loss: 3.8455e-05 - mean_absolute_error: 0.0046121/126 [===========================>..] - ETA: 0s - loss: 3.8146e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8717e-05 - mean_absolute_error: 0.0046 - val_loss: 1.1061e-04 - val_mean_absolute_error: 0.0084
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 4.9054e-05 - mean_absolute_error: 0.0051 25/126 [====>.........................] - ETA: 0s - loss: 3.7351e-05 - mean_absolute_error: 0.0046 50/126 [==========>...................] - ETA: 0s - loss: 3.7275e-05 - mean_absolute_error: 0.0047 75/126 [================>.............] - ETA: 0s - loss: 3.8717e-05 - mean_absolute_error: 0.0047100/126 [======================>.......] - ETA: 0s - loss: 3.7898e-05 - mean_absolute_error: 0.0046125/126 [============================>.] - ETA: 0s - loss: 3.7628e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7678e-05 - mean_absolute_error: 0.0046 - val_loss: 1.0629e-04 - val_mean_absolute_error: 0.0082
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 4.2883e-05 - mean_absolute_error: 0.0052 26/126 [=====>........................] - ETA: 0s - loss: 4.2321e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 4.2625e-05 - mean_absolute_error: 0.0049 76/126 [=================>............] - ETA: 0s - loss: 4.1338e-05 - mean_absolute_error: 0.0048100/126 [======================>.......] - ETA: 0s - loss: 3.9304e-05 - mean_absolute_error: 0.0047125/126 [============================>.] - ETA: 0s - loss: 3.8655e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8621e-05 - mean_absolute_error: 0.0046 - val_loss: 8.0428e-05 - val_mean_absolute_error: 0.0066
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 2.2511e-05 - mean_absolute_error: 0.0038 26/126 [=====>........................] - ETA: 0s - loss: 3.4739e-05 - mean_absolute_error: 0.0043 52/126 [===========>..................] - ETA: 0s - loss: 3.7335e-05 - mean_absolute_error: 0.0044 76/126 [=================>............] - ETA: 0s - loss: 3.7062e-05 - mean_absolute_error: 0.0044 97/126 [======================>.......] - ETA: 0s - loss: 3.6207e-05 - mean_absolute_error: 0.0044121/126 [===========================>..] - ETA: 0s - loss: 3.6383e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6087e-05 - mean_absolute_error: 0.0043 - val_loss: 1.2687e-04 - val_mean_absolute_error: 0.0091
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 4.8384e-05 - mean_absolute_error: 0.0056 25/126 [====>.........................] - ETA: 0s - loss: 3.3213e-05 - mean_absolute_error: 0.0044 48/126 [==========>...................] - ETA: 0s - loss: 3.5083e-05 - mean_absolute_error: 0.0044 73/126 [================>.............] - ETA: 0s - loss: 3.7064e-05 - mean_absolute_error: 0.0045 97/126 [======================>.......] - ETA: 0s - loss: 3.7494e-05 - mean_absolute_error: 0.0045122/126 [============================>.] - ETA: 0s - loss: 3.6067e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.5780e-05 - mean_absolute_error: 0.0044 - val_loss: 1.1228e-04 - val_mean_absolute_error: 0.0084
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 2.5714e-05 - mean_absolute_error: 0.0037 25/126 [====>.........................] - ETA: 0s - loss: 3.3742e-05 - mean_absolute_error: 0.0042 50/126 [==========>...................] - ETA: 0s - loss: 3.5673e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.7260e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.7750e-05 - mean_absolute_error: 0.0045125/126 [============================>.] - ETA: 0s - loss: 3.9096e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9088e-05 - mean_absolute_error: 0.0047 - val_loss: 7.5655e-05 - val_mean_absolute_error: 0.0063
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 3.5102e-05 - mean_absolute_error: 0.0047 25/126 [====>.........................] - ETA: 0s - loss: 4.2062e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 3.8702e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 3.7280e-05 - mean_absolute_error: 0.0044100/126 [======================>.......] - ETA: 0s - loss: 3.7069e-05 - mean_absolute_error: 0.0044123/126 [============================>.] - ETA: 0s - loss: 3.7002e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6963e-05 - mean_absolute_error: 0.0045 - val_loss: 7.3375e-05 - val_mean_absolute_error: 0.0060
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 3.6786e-05 - mean_absolute_error: 0.0050 24/126 [====>.........................] - ETA: 0s - loss: 4.4655e-05 - mean_absolute_error: 0.0051 49/126 [==========>...................] - ETA: 0s - loss: 4.5790e-05 - mean_absolute_error: 0.0053 72/126 [================>.............] - ETA: 0s - loss: 4.3952e-05 - mean_absolute_error: 0.0050 96/126 [=====================>........] - ETA: 0s - loss: 4.2499e-05 - mean_absolute_error: 0.0050119/126 [===========================>..] - ETA: 0s - loss: 4.0994e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 3ms/step - loss: 4.1826e-05 - mean_absolute_error: 0.0049 - val_loss: 9.7659e-05 - val_mean_absolute_error: 0.0073
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 6.4450e-05 - mean_absolute_error: 0.0071 21/126 [====>.........................] - ETA: 0s - loss: 3.9500e-05 - mean_absolute_error: 0.0047 38/126 [========>.....................] - ETA: 0s - loss: 4.3253e-05 - mean_absolute_error: 0.0050 54/126 [===========>..................] - ETA: 0s - loss: 4.0755e-05 - mean_absolute_error: 0.0048 75/126 [================>.............] - ETA: 0s - loss: 3.9724e-05 - mean_absolute_error: 0.0048 99/126 [======================>.......] - ETA: 0s - loss: 4.0671e-05 - mean_absolute_error: 0.0049123/126 [============================>.] - ETA: 0s - loss: 4.0231e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 4.0217e-05 - mean_absolute_error: 0.0048 - val_loss: 9.1046e-05 - val_mean_absolute_error: 0.0073
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 4.0450e-05 - mean_absolute_error: 0.0048 26/126 [=====>........................] - ETA: 0s - loss: 3.9412e-05 - mean_absolute_error: 0.0047 51/126 [===========>..................] - ETA: 0s - loss: 3.9587e-05 - mean_absolute_error: 0.0047 77/126 [=================>............] - ETA: 0s - loss: 3.9716e-05 - mean_absolute_error: 0.0048102/126 [=======================>......] - ETA: 0s - loss: 3.9492e-05 - mean_absolute_error: 0.0047125/126 [============================>.] - ETA: 0s - loss: 3.8847e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8970e-05 - mean_absolute_error: 0.0047 - val_loss: 9.3853e-05 - val_mean_absolute_error: 0.0075
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 3.5558e-05 - mean_absolute_error: 0.0048 25/126 [====>.........................] - ETA: 0s - loss: 3.7771e-05 - mean_absolute_error: 0.0045 50/126 [==========>...................] - ETA: 0s - loss: 3.6540e-05 - mean_absolute_error: 0.0045 73/126 [================>.............] - ETA: 0s - loss: 4.0541e-05 - mean_absolute_error: 0.0047 98/126 [======================>.......] - ETA: 0s - loss: 4.0702e-05 - mean_absolute_error: 0.0048122/126 [============================>.] - ETA: 0s - loss: 4.0889e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0908e-05 - mean_absolute_error: 0.0048 - val_loss: 7.4892e-05 - val_mean_absolute_error: 0.0061
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 6.7592e-05 - mean_absolute_error: 0.0067 25/126 [====>.........................] - ETA: 0s - loss: 4.2101e-05 - mean_absolute_error: 0.0050 50/126 [==========>...................] - ETA: 0s - loss: 4.2253e-05 - mean_absolute_error: 0.0050 75/126 [================>.............] - ETA: 0s - loss: 3.9541e-05 - mean_absolute_error: 0.0047100/126 [======================>.......] - ETA: 0s - loss: 3.8942e-05 - mean_absolute_error: 0.0046125/126 [============================>.] - ETA: 0s - loss: 3.8262e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.8187e-05 - mean_absolute_error: 0.0045 - val_loss: 1.1540e-04 - val_mean_absolute_error: 0.0086
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 2.8060e-05 - mean_absolute_error: 0.0039 25/126 [====>.........................] - ETA: 0s - loss: 3.5001e-05 - mean_absolute_error: 0.0043 50/126 [==========>...................] - ETA: 0s - loss: 3.7061e-05 - mean_absolute_error: 0.0043 73/126 [================>.............] - ETA: 0s - loss: 3.8171e-05 - mean_absolute_error: 0.0045 95/126 [=====================>........] - ETA: 0s - loss: 3.7905e-05 - mean_absolute_error: 0.0045109/126 [========================>.....] - ETA: 0s - loss: 3.7550e-05 - mean_absolute_error: 0.0045123/126 [============================>.] - ETA: 0s - loss: 3.6969e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.7147e-05 - mean_absolute_error: 0.0045 - val_loss: 1.1002e-04 - val_mean_absolute_error: 0.0084
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 3.5629e-05 - mean_absolute_error: 0.0044 16/126 [==>...........................] - ETA: 0s - loss: 3.3950e-05 - mean_absolute_error: 0.0044 32/126 [======>.......................] - ETA: 0s - loss: 3.5987e-05 - mean_absolute_error: 0.0043 48/126 [==========>...................] - ETA: 0s - loss: 3.6625e-05 - mean_absolute_error: 0.0044 64/126 [==============>...............] - ETA: 0s - loss: 3.6163e-05 - mean_absolute_error: 0.0044 82/126 [==================>...........] - ETA: 0s - loss: 3.7609e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.6615e-05 - mean_absolute_error: 0.0045120/126 [===========================>..] - ETA: 0s - loss: 3.7436e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 3ms/step - loss: 3.8270e-05 - mean_absolute_error: 0.0046 - val_loss: 7.1827e-05 - val_mean_absolute_error: 0.0060
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 4.8203e-05 - mean_absolute_error: 0.0055 21/126 [====>.........................] - ETA: 0s - loss: 4.4865e-05 - mean_absolute_error: 0.0051 41/126 [========>.....................] - ETA: 0s - loss: 4.3670e-05 - mean_absolute_error: 0.0049 64/126 [==============>...............] - ETA: 0s - loss: 4.8170e-05 - mean_absolute_error: 0.0053 88/126 [===================>..........] - ETA: 0s - loss: 4.5136e-05 - mean_absolute_error: 0.0051112/126 [=========================>....] - ETA: 0s - loss: 4.6655e-05 - mean_absolute_error: 0.0052126/126 [==============================] - 0s 3ms/step - loss: 4.8573e-05 - mean_absolute_error: 0.0054 - val_loss: 1.0042e-04 - val_mean_absolute_error: 0.0079
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 3.2196e-05 - mean_absolute_error: 0.0042 26/126 [=====>........................] - ETA: 0s - loss: 4.5806e-05 - mean_absolute_error: 0.0050 51/126 [===========>..................] - ETA: 0s - loss: 4.0176e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 3.7796e-05 - mean_absolute_error: 0.0045 99/126 [======================>.......] - ETA: 0s - loss: 3.8407e-05 - mean_absolute_error: 0.0045123/126 [============================>.] - ETA: 0s - loss: 3.8017e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.7956e-05 - mean_absolute_error: 0.0045 - val_loss: 7.5978e-05 - val_mean_absolute_error: 0.0064
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 1.4349e-05 - mean_absolute_error: 0.0029 26/126 [=====>........................] - ETA: 0s - loss: 5.9970e-05 - mean_absolute_error: 0.0061 49/126 [==========>...................] - ETA: 0s - loss: 5.0712e-05 - mean_absolute_error: 0.0054 73/126 [================>.............] - ETA: 0s - loss: 4.5040e-05 - mean_absolute_error: 0.0050 98/126 [======================>.......] - ETA: 0s - loss: 4.3214e-05 - mean_absolute_error: 0.0049121/126 [===========================>..] - ETA: 0s - loss: 4.1984e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.1924e-05 - mean_absolute_error: 0.0049 - val_loss: 1.3637e-04 - val_mean_absolute_error: 0.0096
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 3.6723e-05 - mean_absolute_error: 0.0050 26/126 [=====>........................] - ETA: 0s - loss: 3.7329e-05 - mean_absolute_error: 0.0044 50/126 [==========>...................] - ETA: 0s - loss: 3.6902e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.8225e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.7029e-05 - mean_absolute_error: 0.0044125/126 [============================>.] - ETA: 0s - loss: 3.6825e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6905e-05 - mean_absolute_error: 0.0044 - val_loss: 1.2147e-04 - val_mean_absolute_error: 0.0089
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 5.6109e-05 - mean_absolute_error: 0.0061 24/126 [====>.........................] - ETA: 0s - loss: 3.4095e-05 - mean_absolute_error: 0.0042 49/126 [==========>...................] - ETA: 0s - loss: 3.5373e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.9079e-05 - mean_absolute_error: 0.0047 99/126 [======================>.......] - ETA: 0s - loss: 4.1076e-05 - mean_absolute_error: 0.0049124/126 [============================>.] - ETA: 0s - loss: 4.2744e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 2ms/step - loss: 4.3092e-05 - mean_absolute_error: 0.0050 - val_loss: 7.1205e-05 - val_mean_absolute_error: 0.0060
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 4.1058e-05 - mean_absolute_error: 0.0053 24/126 [====>.........................] - ETA: 0s - loss: 4.0783e-05 - mean_absolute_error: 0.0049 49/126 [==========>...................] - ETA: 0s - loss: 4.2131e-05 - mean_absolute_error: 0.0049 74/126 [================>.............] - ETA: 0s - loss: 4.0852e-05 - mean_absolute_error: 0.0048 97/126 [======================>.......] - ETA: 0s - loss: 4.0800e-05 - mean_absolute_error: 0.0048122/126 [============================>.] - ETA: 0s - loss: 3.9922e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9590e-05 - mean_absolute_error: 0.0047 - val_loss: 7.9620e-05 - val_mean_absolute_error: 0.0066
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 2.3186e-05 - mean_absolute_error: 0.0034 24/126 [====>.........................] - ETA: 0s - loss: 4.8498e-05 - mean_absolute_error: 0.0052 49/126 [==========>...................] - ETA: 0s - loss: 4.3560e-05 - mean_absolute_error: 0.0049 74/126 [================>.............] - ETA: 0s - loss: 4.0874e-05 - mean_absolute_error: 0.0048 99/126 [======================>.......] - ETA: 0s - loss: 4.1566e-05 - mean_absolute_error: 0.0049124/126 [============================>.] - ETA: 0s - loss: 4.1492e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.1913e-05 - mean_absolute_error: 0.0049 - val_loss: 7.1175e-05 - val_mean_absolute_error: 0.0059
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 3.7354e-05 - mean_absolute_error: 0.0054 26/126 [=====>........................] - ETA: 0s - loss: 6.4751e-05 - mean_absolute_error: 0.0066 51/126 [===========>..................] - ETA: 0s - loss: 5.8257e-05 - mean_absolute_error: 0.0060 75/126 [================>.............] - ETA: 0s - loss: 5.0053e-05 - mean_absolute_error: 0.0055100/126 [======================>.......] - ETA: 0s - loss: 4.7775e-05 - mean_absolute_error: 0.0053124/126 [============================>.] - ETA: 0s - loss: 4.7819e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 2ms/step - loss: 4.7914e-05 - mean_absolute_error: 0.0053 - val_loss: 6.9380e-05 - val_mean_absolute_error: 0.0058
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 4.9781e-05 - mean_absolute_error: 0.0059 23/126 [====>.........................] - ETA: 0s - loss: 5.8127e-05 - mean_absolute_error: 0.0060 48/126 [==========>...................] - ETA: 0s - loss: 4.4084e-05 - mean_absolute_error: 0.0050 73/126 [================>.............] - ETA: 0s - loss: 4.3305e-05 - mean_absolute_error: 0.0050 98/126 [======================>.......] - ETA: 0s - loss: 4.2172e-05 - mean_absolute_error: 0.0049121/126 [===========================>..] - ETA: 0s - loss: 4.1282e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0765e-05 - mean_absolute_error: 0.0048 - val_loss: 7.2005e-05 - val_mean_absolute_error: 0.0061
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 3.4020e-05 - mean_absolute_error: 0.0044 25/126 [====>.........................] - ETA: 0s - loss: 3.6910e-05 - mean_absolute_error: 0.0045 50/126 [==========>...................] - ETA: 0s - loss: 3.6951e-05 - mean_absolute_error: 0.0044 75/126 [================>.............] - ETA: 0s - loss: 4.1730e-05 - mean_absolute_error: 0.0048 99/126 [======================>.......] - ETA: 0s - loss: 4.5624e-05 - mean_absolute_error: 0.0051124/126 [============================>.] - ETA: 0s - loss: 4.6393e-05 - mean_absolute_error: 0.0052126/126 [==============================] - 0s 2ms/step - loss: 4.6368e-05 - mean_absolute_error: 0.0051 - val_loss: 8.9895e-05 - val_mean_absolute_error: 0.0073
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 2.4407e-05 - mean_absolute_error: 0.0038 26/126 [=====>........................] - ETA: 0s - loss: 4.6857e-05 - mean_absolute_error: 0.0053 52/126 [===========>..................] - ETA: 0s - loss: 5.1354e-05 - mean_absolute_error: 0.0056 77/126 [=================>............] - ETA: 0s - loss: 4.7948e-05 - mean_absolute_error: 0.0054102/126 [=======================>......] - ETA: 0s - loss: 4.8711e-05 - mean_absolute_error: 0.0054126/126 [==============================] - 0s 2ms/step - loss: 4.8014e-05 - mean_absolute_error: 0.0054 - val_loss: 6.9862e-05 - val_mean_absolute_error: 0.0060
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 3.2392e-05 - mean_absolute_error: 0.0042 26/126 [=====>........................] - ETA: 0s - loss: 3.4894e-05 - mean_absolute_error: 0.0043 52/126 [===========>..................] - ETA: 0s - loss: 3.4642e-05 - mean_absolute_error: 0.0044 75/126 [================>.............] - ETA: 0s - loss: 3.6142e-05 - mean_absolute_error: 0.0045 98/126 [======================>.......] - ETA: 0s - loss: 3.9664e-05 - mean_absolute_error: 0.0048122/126 [============================>.] - ETA: 0s - loss: 4.0307e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0275e-05 - mean_absolute_error: 0.0048 - val_loss: 1.1128e-04 - val_mean_absolute_error: 0.0085
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 7.0895e-05 - mean_absolute_error: 0.0067 23/126 [====>.........................] - ETA: 0s - loss: 3.9719e-05 - mean_absolute_error: 0.0046 48/126 [==========>...................] - ETA: 0s - loss: 4.4412e-05 - mean_absolute_error: 0.0049 73/126 [================>.............] - ETA: 0s - loss: 4.8194e-05 - mean_absolute_error: 0.0053 98/126 [======================>.......] - ETA: 0s - loss: 5.0532e-05 - mean_absolute_error: 0.0055123/126 [============================>.] - ETA: 0s - loss: 5.2190e-05 - mean_absolute_error: 0.0056126/126 [==============================] - 0s 2ms/step - loss: 5.2272e-05 - mean_absolute_error: 0.0056 - val_loss: 9.3654e-05 - val_mean_absolute_error: 0.0076
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 3.4387e-05 - mean_absolute_error: 0.0049 26/126 [=====>........................] - ETA: 0s - loss: 4.7499e-05 - mean_absolute_error: 0.0053 51/126 [===========>..................] - ETA: 0s - loss: 4.7795e-05 - mean_absolute_error: 0.0054 76/126 [=================>............] - ETA: 0s - loss: 4.6112e-05 - mean_absolute_error: 0.0053101/126 [=======================>......] - ETA: 0s - loss: 4.4384e-05 - mean_absolute_error: 0.0052123/126 [============================>.] - ETA: 0s - loss: 4.3143e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 2ms/step - loss: 4.2739e-05 - mean_absolute_error: 0.0050 - val_loss: 1.0867e-04 - val_mean_absolute_error: 0.0084
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 3.9100e-05 - mean_absolute_error: 0.0049 24/126 [====>.........................] - ETA: 0s - loss: 3.6226e-05 - mean_absolute_error: 0.0046 47/126 [==========>...................] - ETA: 0s - loss: 4.3387e-05 - mean_absolute_error: 0.0050 68/126 [===============>..............] - ETA: 0s - loss: 4.2676e-05 - mean_absolute_error: 0.0049 90/126 [====================>.........] - ETA: 0s - loss: 4.0271e-05 - mean_absolute_error: 0.0047114/126 [==========================>...] - ETA: 0s - loss: 4.0703e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 3ms/step - loss: 4.0104e-05 - mean_absolute_error: 0.0047 - val_loss: 7.0914e-05 - val_mean_absolute_error: 0.0061
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 8.2244e-05 - mean_absolute_error: 0.0055 25/126 [====>.........................] - ETA: 0s - loss: 4.0667e-05 - mean_absolute_error: 0.0047 47/126 [==========>...................] - ETA: 0s - loss: 4.0336e-05 - mean_absolute_error: 0.0046 72/126 [================>.............] - ETA: 0s - loss: 3.9357e-05 - mean_absolute_error: 0.0047 96/126 [=====================>........] - ETA: 0s - loss: 4.2760e-05 - mean_absolute_error: 0.0049121/126 [===========================>..] - ETA: 0s - loss: 4.2831e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.3076e-05 - mean_absolute_error: 0.0050 - val_loss: 6.9367e-05 - val_mean_absolute_error: 0.0058
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 2.6118e-05 - mean_absolute_error: 0.0043 26/126 [=====>........................] - ETA: 0s - loss: 4.1945e-05 - mean_absolute_error: 0.0049 51/126 [===========>..................] - ETA: 0s - loss: 3.9652e-05 - mean_absolute_error: 0.0048 76/126 [=================>............] - ETA: 0s - loss: 3.9315e-05 - mean_absolute_error: 0.0047101/126 [=======================>......] - ETA: 0s - loss: 3.9707e-05 - mean_absolute_error: 0.0047126/126 [==============================] - ETA: 0s - loss: 3.9657e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9657e-05 - mean_absolute_error: 0.0047 - val_loss: 1.4885e-04 - val_mean_absolute_error: 0.0102
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 3.3530e-05 - mean_absolute_error: 0.0052 26/126 [=====>........................] - ETA: 0s - loss: 5.4934e-05 - mean_absolute_error: 0.0060 52/126 [===========>..................] - ETA: 0s - loss: 5.3114e-05 - mean_absolute_error: 0.0058 76/126 [=================>............] - ETA: 0s - loss: 5.4852e-05 - mean_absolute_error: 0.0058101/126 [=======================>......] - ETA: 0s - loss: 4.9727e-05 - mean_absolute_error: 0.0054125/126 [============================>.] - ETA: 0s - loss: 4.7270e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 2ms/step - loss: 4.7177e-05 - mean_absolute_error: 0.0053 - val_loss: 8.7236e-05 - val_mean_absolute_error: 0.0072
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 3.3953e-05 - mean_absolute_error: 0.0045 24/126 [====>.........................] - ETA: 0s - loss: 4.0928e-05 - mean_absolute_error: 0.0049 49/126 [==========>...................] - ETA: 0s - loss: 3.6608e-05 - mean_absolute_error: 0.0046 73/126 [================>.............] - ETA: 0s - loss: 3.6575e-05 - mean_absolute_error: 0.0045 96/126 [=====================>........] - ETA: 0s - loss: 3.5486e-05 - mean_absolute_error: 0.0044121/126 [===========================>..] - ETA: 0s - loss: 3.6779e-05 - mean_absolute_error: 0.0045126/126 [==============================] - 0s 2ms/step - loss: 3.7005e-05 - mean_absolute_error: 0.0045 - val_loss: 7.6675e-05 - val_mean_absolute_error: 0.0065
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 2.9754e-05 - mean_absolute_error: 0.0037 25/126 [====>.........................] - ETA: 0s - loss: 3.4770e-05 - mean_absolute_error: 0.0042 50/126 [==========>...................] - ETA: 0s - loss: 3.6336e-05 - mean_absolute_error: 0.0044 74/126 [================>.............] - ETA: 0s - loss: 3.6826e-05 - mean_absolute_error: 0.0045 99/126 [======================>.......] - ETA: 0s - loss: 3.8245e-05 - mean_absolute_error: 0.0046124/126 [============================>.] - ETA: 0s - loss: 4.5297e-05 - mean_absolute_error: 0.0051126/126 [==============================] - 0s 2ms/step - loss: 4.5284e-05 - mean_absolute_error: 0.0051 - val_loss: 1.7779e-04 - val_mean_absolute_error: 0.0115
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 1.0537e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 3.6681e-05 - mean_absolute_error: 0.0045 52/126 [===========>..................] - ETA: 0s - loss: 3.7148e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 3.7859e-05 - mean_absolute_error: 0.0045102/126 [=======================>......] - ETA: 0s - loss: 3.7245e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7755e-05 - mean_absolute_error: 0.0046 - val_loss: 9.1159e-05 - val_mean_absolute_error: 0.0075
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 2.8917e-05 - mean_absolute_error: 0.0039 24/126 [====>.........................] - ETA: 0s - loss: 3.8380e-05 - mean_absolute_error: 0.0046 49/126 [==========>...................] - ETA: 0s - loss: 3.7110e-05 - mean_absolute_error: 0.0045 74/126 [================>.............] - ETA: 0s - loss: 3.7100e-05 - mean_absolute_error: 0.0045 99/126 [======================>.......] - ETA: 0s - loss: 3.6576e-05 - mean_absolute_error: 0.0045119/126 [===========================>..] - ETA: 0s - loss: 3.5751e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5979e-05 - mean_absolute_error: 0.0044 - val_loss: 1.0811e-04 - val_mean_absolute_error: 0.0084
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 2.5506e-05 - mean_absolute_error: 0.0041 24/126 [====>.........................] - ETA: 0s - loss: 3.7378e-05 - mean_absolute_error: 0.0044 49/126 [==========>...................] - ETA: 0s - loss: 3.5894e-05 - mean_absolute_error: 0.0044 72/126 [================>.............] - ETA: 0s - loss: 3.6739e-05 - mean_absolute_error: 0.0045 97/126 [======================>.......] - ETA: 0s - loss: 3.7541e-05 - mean_absolute_error: 0.0046121/126 [===========================>..] - ETA: 0s - loss: 4.0396e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0704e-05 - mean_absolute_error: 0.0048 - val_loss: 6.6081e-05 - val_mean_absolute_error: 0.0057
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 2.1547e-05 - mean_absolute_error: 0.0037 25/126 [====>.........................] - ETA: 0s - loss: 3.5807e-05 - mean_absolute_error: 0.0044 50/126 [==========>...................] - ETA: 0s - loss: 3.5615e-05 - mean_absolute_error: 0.0045 75/126 [================>.............] - ETA: 0s - loss: 3.7681e-05 - mean_absolute_error: 0.0047100/126 [======================>.......] - ETA: 0s - loss: 4.2601e-05 - mean_absolute_error: 0.0050125/126 [============================>.] - ETA: 0s - loss: 4.4772e-05 - mean_absolute_error: 0.0051126/126 [==============================] - 0s 2ms/step - loss: 4.4748e-05 - mean_absolute_error: 0.0051 - val_loss: 6.8091e-05 - val_mean_absolute_error: 0.0058
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 5.6583e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 3.5215e-05 - mean_absolute_error: 0.0043 50/126 [==========>...................] - ETA: 0s - loss: 3.5074e-05 - mean_absolute_error: 0.0043 74/126 [================>.............] - ETA: 0s - loss: 3.6910e-05 - mean_absolute_error: 0.0044 98/126 [======================>.......] - ETA: 0s - loss: 3.9167e-05 - mean_absolute_error: 0.0046123/126 [============================>.] - ETA: 0s - loss: 4.0774e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.0441e-05 - mean_absolute_error: 0.0048 - val_loss: 7.5046e-05 - val_mean_absolute_error: 0.0062
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 4.0521e-05 - mean_absolute_error: 0.0055 25/126 [====>.........................] - ETA: 0s - loss: 4.0123e-05 - mean_absolute_error: 0.0048 50/126 [==========>...................] - ETA: 0s - loss: 4.1655e-05 - mean_absolute_error: 0.0049 75/126 [================>.............] - ETA: 0s - loss: 4.3824e-05 - mean_absolute_error: 0.0051100/126 [======================>.......] - ETA: 0s - loss: 4.4074e-05 - mean_absolute_error: 0.0051124/126 [============================>.] - ETA: 0s - loss: 4.2711e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 2ms/step - loss: 4.2775e-05 - mean_absolute_error: 0.0050 - val_loss: 6.6967e-05 - val_mean_absolute_error: 0.0057
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 2.3192e-05 - mean_absolute_error: 0.0041 26/126 [=====>........................] - ETA: 0s - loss: 3.5079e-05 - mean_absolute_error: 0.0044 52/126 [===========>..................] - ETA: 0s - loss: 3.7165e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 3.9295e-05 - mean_absolute_error: 0.0047102/126 [=======================>......] - ETA: 0s - loss: 3.9666e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 4.3172e-05 - mean_absolute_error: 0.0050 - val_loss: 1.1159e-04 - val_mean_absolute_error: 0.0086
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 5.1845e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 4.1048e-05 - mean_absolute_error: 0.0050 51/126 [===========>..................] - ETA: 0s - loss: 4.1154e-05 - mean_absolute_error: 0.0050 75/126 [================>.............] - ETA: 0s - loss: 3.9690e-05 - mean_absolute_error: 0.0048 97/126 [======================>.......] - ETA: 0s - loss: 3.8984e-05 - mean_absolute_error: 0.0047121/126 [===========================>..] - ETA: 0s - loss: 3.8537e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8349e-05 - mean_absolute_error: 0.0047 - val_loss: 6.6980e-05 - val_mean_absolute_error: 0.0057
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 3.3105e-05 - mean_absolute_error: 0.0045 26/126 [=====>........................] - ETA: 0s - loss: 3.8971e-05 - mean_absolute_error: 0.0048 48/126 [==========>...................] - ETA: 0s - loss: 3.7453e-05 - mean_absolute_error: 0.0047 73/126 [================>.............] - ETA: 0s - loss: 3.9423e-05 - mean_absolute_error: 0.0048 98/126 [======================>.......] - ETA: 0s - loss: 4.0700e-05 - mean_absolute_error: 0.0049122/126 [============================>.] - ETA: 0s - loss: 4.1275e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 2ms/step - loss: 4.1603e-05 - mean_absolute_error: 0.0050 - val_loss: 6.4667e-05 - val_mean_absolute_error: 0.0057
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 3.8153e-05 - mean_absolute_error: 0.0046 25/126 [====>.........................] - ETA: 0s - loss: 3.5090e-05 - mean_absolute_error: 0.0042 49/126 [==========>...................] - ETA: 0s - loss: 3.6375e-05 - mean_absolute_error: 0.0044 73/126 [================>.............] - ETA: 0s - loss: 3.5488e-05 - mean_absolute_error: 0.0044 98/126 [======================>.......] - ETA: 0s - loss: 3.5841e-05 - mean_absolute_error: 0.0044123/126 [============================>.] - ETA: 0s - loss: 3.5547e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.5861e-05 - mean_absolute_error: 0.0044 - val_loss: 8.4313e-05 - val_mean_absolute_error: 0.0071
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 3.4693e-05 - mean_absolute_error: 0.0043 26/126 [=====>........................] - ETA: 0s - loss: 4.0068e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 3.9181e-05 - mean_absolute_error: 0.0047 76/126 [=================>............] - ETA: 0s - loss: 4.2797e-05 - mean_absolute_error: 0.0049101/126 [=======================>......] - ETA: 0s - loss: 4.2458e-05 - mean_absolute_error: 0.0049122/126 [============================>.] - ETA: 0s - loss: 4.2238e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.2017e-05 - mean_absolute_error: 0.0049 - val_loss: 6.5502e-05 - val_mean_absolute_error: 0.0056
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 4.8681e-05 - mean_absolute_error: 0.0044 24/126 [====>.........................] - ETA: 0s - loss: 5.6258e-05 - mean_absolute_error: 0.0058 48/126 [==========>...................] - ETA: 0s - loss: 5.1445e-05 - mean_absolute_error: 0.0057 71/126 [===============>..............] - ETA: 0s - loss: 5.0875e-05 - mean_absolute_error: 0.0056 95/126 [=====================>........] - ETA: 0s - loss: 4.7748e-05 - mean_absolute_error: 0.0054119/126 [===========================>..] - ETA: 0s - loss: 4.6609e-05 - mean_absolute_error: 0.0053126/126 [==============================] - 0s 2ms/step - loss: 4.6280e-05 - mean_absolute_error: 0.0053 - val_loss: 7.8991e-05 - val_mean_absolute_error: 0.0068
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 6.4856e-05 - mean_absolute_error: 0.0047 24/126 [====>.........................] - ETA: 0s - loss: 3.8722e-05 - mean_absolute_error: 0.0046 48/126 [==========>...................] - ETA: 0s - loss: 3.6493e-05 - mean_absolute_error: 0.0044 72/126 [================>.............] - ETA: 0s - loss: 3.6054e-05 - mean_absolute_error: 0.0044 97/126 [======================>.......] - ETA: 0s - loss: 3.7838e-05 - mean_absolute_error: 0.0046122/126 [============================>.] - ETA: 0s - loss: 3.8923e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.9021e-05 - mean_absolute_error: 0.0047 - val_loss: 8.5697e-05 - val_mean_absolute_error: 0.0072
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 2.2135e-05 - mean_absolute_error: 0.0038 26/126 [=====>........................] - ETA: 0s - loss: 4.6513e-05 - mean_absolute_error: 0.0053 51/126 [===========>..................] - ETA: 0s - loss: 4.5281e-05 - mean_absolute_error: 0.0051 76/126 [=================>............] - ETA: 0s - loss: 4.4505e-05 - mean_absolute_error: 0.0051101/126 [=======================>......] - ETA: 0s - loss: 4.1768e-05 - mean_absolute_error: 0.0049126/126 [==============================] - ETA: 0s - loss: 4.1227e-05 - mean_absolute_error: 0.0049126/126 [==============================] - 0s 2ms/step - loss: 4.1227e-05 - mean_absolute_error: 0.0049 - val_loss: 6.4623e-05 - val_mean_absolute_error: 0.0056
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 7.2633e-05 - mean_absolute_error: 0.0050 23/126 [====>.........................] - ETA: 0s - loss: 3.1484e-05 - mean_absolute_error: 0.0039 47/126 [==========>...................] - ETA: 0s - loss: 3.3776e-05 - mean_absolute_error: 0.0042 72/126 [================>.............] - ETA: 0s - loss: 3.4099e-05 - mean_absolute_error: 0.0043 96/126 [=====================>........] - ETA: 0s - loss: 3.4352e-05 - mean_absolute_error: 0.0043119/126 [===========================>..] - ETA: 0s - loss: 3.8076e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.9399e-05 - mean_absolute_error: 0.0047 - val_loss: 6.6113e-05 - val_mean_absolute_error: 0.0059
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 4.4629e-05 - mean_absolute_error: 0.0049 26/126 [=====>........................] - ETA: 0s - loss: 5.3000e-05 - mean_absolute_error: 0.0056 52/126 [===========>..................] - ETA: 0s - loss: 4.8745e-05 - mean_absolute_error: 0.0054 77/126 [=================>............] - ETA: 0s - loss: 4.4103e-05 - mean_absolute_error: 0.0051102/126 [=======================>......] - ETA: 0s - loss: 4.0590e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 3.9893e-05 - mean_absolute_error: 0.0047 - val_loss: 6.3907e-05 - val_mean_absolute_error: 0.0056
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 2.9126e-05 - mean_absolute_error: 0.0042 26/126 [=====>........................] - ETA: 0s - loss: 3.7429e-05 - mean_absolute_error: 0.0046 50/126 [==========>...................] - ETA: 0s - loss: 3.8223e-05 - mean_absolute_error: 0.0047 75/126 [================>.............] - ETA: 0s - loss: 3.5912e-05 - mean_absolute_error: 0.0046100/126 [======================>.......] - ETA: 0s - loss: 3.7541e-05 - mean_absolute_error: 0.0046125/126 [============================>.] - ETA: 0s - loss: 3.7448e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.7355e-05 - mean_absolute_error: 0.0046 - val_loss: 7.9624e-05 - val_mean_absolute_error: 0.0069
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 2.4574e-05 - mean_absolute_error: 0.0036 26/126 [=====>........................] - ETA: 0s - loss: 4.5789e-05 - mean_absolute_error: 0.0054 46/126 [=========>....................] - ETA: 0s - loss: 5.0152e-05 - mean_absolute_error: 0.0056 66/126 [==============>...............] - ETA: 0s - loss: 4.6388e-05 - mean_absolute_error: 0.0053 84/126 [===================>..........] - ETA: 0s - loss: 4.3478e-05 - mean_absolute_error: 0.0050105/126 [========================>.....] - ETA: 0s - loss: 4.1261e-05 - mean_absolute_error: 0.0049123/126 [============================>.] - ETA: 0s - loss: 4.0098e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 3ms/step - loss: 3.9851e-05 - mean_absolute_error: 0.0047 - val_loss: 6.6615e-05 - val_mean_absolute_error: 0.0059
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 5.9460e-05 - mean_absolute_error: 0.0053 20/126 [===>..........................] - ETA: 0s - loss: 3.4045e-05 - mean_absolute_error: 0.0042 39/126 [========>.....................] - ETA: 0s - loss: 3.6192e-05 - mean_absolute_error: 0.0043 62/126 [=============>................] - ETA: 0s - loss: 3.6475e-05 - mean_absolute_error: 0.0044 86/126 [===================>..........] - ETA: 0s - loss: 3.5239e-05 - mean_absolute_error: 0.0044110/126 [=========================>....] - ETA: 0s - loss: 3.5934e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 3ms/step - loss: 3.5391e-05 - mean_absolute_error: 0.0044 - val_loss: 6.3081e-05 - val_mean_absolute_error: 0.0056
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 2.2889e-05 - mean_absolute_error: 0.0034 26/126 [=====>........................] - ETA: 0s - loss: 3.6923e-05 - mean_absolute_error: 0.0043 52/126 [===========>..................] - ETA: 0s - loss: 3.9099e-05 - mean_absolute_error: 0.0045 77/126 [=================>............] - ETA: 0s - loss: 3.9747e-05 - mean_absolute_error: 0.0047103/126 [=======================>......] - ETA: 0s - loss: 3.8645e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.6852e-05 - mean_absolute_error: 0.0045 - val_loss: 6.7094e-05 - val_mean_absolute_error: 0.0060
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 1.9732e-05 - mean_absolute_error: 0.0031 26/126 [=====>........................] - ETA: 0s - loss: 3.9307e-05 - mean_absolute_error: 0.0047 49/126 [==========>...................] - ETA: 0s - loss: 3.8797e-05 - mean_absolute_error: 0.0047 74/126 [================>.............] - ETA: 0s - loss: 3.8701e-05 - mean_absolute_error: 0.0047 98/126 [======================>.......] - ETA: 0s - loss: 4.0123e-05 - mean_absolute_error: 0.0048123/126 [============================>.] - ETA: 0s - loss: 3.8349e-05 - mean_absolute_error: 0.0046126/126 [==============================] - 0s 2ms/step - loss: 3.8222e-05 - mean_absolute_error: 0.0046 - val_loss: 6.2711e-05 - val_mean_absolute_error: 0.0055
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 4.5735e-05 - mean_absolute_error: 0.0054 26/126 [=====>........................] - ETA: 0s - loss: 3.4339e-05 - mean_absolute_error: 0.0046 50/126 [==========>...................] - ETA: 0s - loss: 3.6743e-05 - mean_absolute_error: 0.0046 75/126 [================>.............] - ETA: 0s - loss: 3.7314e-05 - mean_absolute_error: 0.0046100/126 [======================>.......] - ETA: 0s - loss: 3.6832e-05 - mean_absolute_error: 0.0046125/126 [============================>.] - ETA: 0s - loss: 3.8645e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8623e-05 - mean_absolute_error: 0.0047 - val_loss: 8.1345e-05 - val_mean_absolute_error: 0.0068
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 6.5335e-05 - mean_absolute_error: 0.0065 26/126 [=====>........................] - ETA: 0s - loss: 3.9170e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 3.4696e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.4278e-05 - mean_absolute_error: 0.0043100/126 [======================>.......] - ETA: 0s - loss: 3.6120e-05 - mean_absolute_error: 0.0044125/126 [============================>.] - ETA: 0s - loss: 3.6198e-05 - mean_absolute_error: 0.0044126/126 [==============================] - 0s 2ms/step - loss: 3.6147e-05 - mean_absolute_error: 0.0044 - val_loss: 8.2674e-05 - val_mean_absolute_error: 0.0071
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 4.5221e-05 - mean_absolute_error: 0.0058 26/126 [=====>........................] - ETA: 0s - loss: 3.6702e-05 - mean_absolute_error: 0.0046 51/126 [===========>..................] - ETA: 0s - loss: 4.1459e-05 - mean_absolute_error: 0.0050 76/126 [=================>............] - ETA: 0s - loss: 4.0449e-05 - mean_absolute_error: 0.0049 99/126 [======================>.......] - ETA: 0s - loss: 4.1458e-05 - mean_absolute_error: 0.0050122/126 [============================>.] - ETA: 0s - loss: 4.2281e-05 - mean_absolute_error: 0.0050126/126 [==============================] - 0s 2ms/step - loss: 4.2001e-05 - mean_absolute_error: 0.0050 - val_loss: 7.6705e-05 - val_mean_absolute_error: 0.0068
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 3.7841e-05 - mean_absolute_error: 0.0047 25/126 [====>.........................] - ETA: 0s - loss: 3.5733e-05 - mean_absolute_error: 0.0045 48/126 [==========>...................] - ETA: 0s - loss: 3.5960e-05 - mean_absolute_error: 0.0044 72/126 [================>.............] - ETA: 0s - loss: 3.5283e-05 - mean_absolute_error: 0.0044 97/126 [======================>.......] - ETA: 0s - loss: 3.6213e-05 - mean_absolute_error: 0.0045122/126 [============================>.] - ETA: 0s - loss: 3.7996e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 0s 2ms/step - loss: 3.8074e-05 - mean_absolute_error: 0.0047 - val_loss: 1.0003e-04 - val_mean_absolute_error: 0.0078
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 6.6030e-05 - mean_absolute_error: 0.0072 25/126 [====>.........................] - ETA: 0s - loss: 3.9438e-05 - mean_absolute_error: 0.0049 51/126 [===========>..................] - ETA: 0s - loss: 3.5845e-05 - mean_absolute_error: 0.0046 76/126 [=================>............] - ETA: 0s - loss: 3.5230e-05 - mean_absolute_error: 0.0045100/126 [======================>.......] - ETA: 0s - loss: 3.8768e-05 - mean_absolute_error: 0.0047125/126 [============================>.] - ETA: 0s - loss: 3.9851e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 3.9941e-05 - mean_absolute_error: 0.0048 - val_loss: 8.6707e-05 - val_mean_absolute_error: 0.0074
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 5.0841e-05 - mean_absolute_error: 0.0055 26/126 [=====>........................] - ETA: 0s - loss: 3.8258e-05 - mean_absolute_error: 0.0047 51/126 [===========>..................] - ETA: 0s - loss: 4.4281e-05 - mean_absolute_error: 0.0051 76/126 [=================>............] - ETA: 0s - loss: 4.0539e-05 - mean_absolute_error: 0.0049101/126 [=======================>......] - ETA: 0s - loss: 4.0277e-05 - mean_absolute_error: 0.0049124/126 [============================>.] - ETA: 0s - loss: 3.8701e-05 - mean_absolute_error: 0.0048126/126 [==============================] - 0s 2ms/step - loss: 3.9069e-05 - mean_absolute_error: 0.0048 - val_loss: 6.0605e-05 - val_mean_absolute_error: 0.0054
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 3.7908e-05 - mean_absolute_error: 0.0050 25/126 [====>.........................] - ETA: 0s - loss: 3.1590e-05 - mean_absolute_error: 0.0041 50/126 [==========>...................] - ETA: 0s - loss: 3.4328e-05 - mean_absolute_error: 0.0043 73/126 [================>.............] - ETA: 0s - loss: 3.5482e-05 - mean_absolute_error: 0.0043 98/126 [======================>.......] - ETA: 0s - loss: 3.5089e-05 - mean_absolute_error: 0.0043123/126 [============================>.] - ETA: 0s - loss: 3.4424e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 2ms/step - loss: 3.5137e-05 - mean_absolute_error: 0.0044 - val_loss: 7.8416e-05 - val_mean_absolute_error: 0.0066
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 5.3064e-05 - mean_absolute_error: 0.0060 25/126 [====>.........................] - ETA: 0s - loss: 4.2522e-05 - mean_absolute_error: 0.0049 41/126 [========>.....................] - ETA: 0s - loss: 4.1741e-05 - mean_absolute_error: 0.0050 52/126 [===========>..................] - ETA: 0s - loss: 4.2711e-05 - mean_absolute_error: 0.0051 63/126 [==============>...............] - ETA: 0s - loss: 4.1331e-05 - mean_absolute_error: 0.0050 72/126 [================>.............] - ETA: 0s - loss: 4.0147e-05 - mean_absolute_error: 0.0049 81/126 [==================>...........] - ETA: 0s - loss: 3.9483e-05 - mean_absolute_error: 0.0048 93/126 [=====================>........] - ETA: 0s - loss: 3.8287e-05 - mean_absolute_error: 0.0048105/126 [========================>.....] - ETA: 0s - loss: 3.8292e-05 - mean_absolute_error: 0.0047116/126 [==========================>...] - ETA: 0s - loss: 3.8217e-05 - mean_absolute_error: 0.0047126/126 [==============================] - 1s 5ms/step - loss: 3.7624e-05 - mean_absolute_error: 0.0046 - val_loss: 6.8370e-05 - val_mean_absolute_error: 0.0060
Epoch 84/100
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Epoch 85/100
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Epoch 86/100
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Epoch 87/100
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Epoch 88/100
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Epoch 89/100
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Epoch 90/100
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Epoch 91/100
  1/126 [..............................] - ETA: 0s - loss: 2.5921e-05 - mean_absolute_error: 0.0039 26/126 [=====>........................] - ETA: 0s - loss: 3.9799e-05 - mean_absolute_error: 0.0048 51/126 [===========>..................] - ETA: 0s - loss: 3.4451e-05 - mean_absolute_error: 0.0043 75/126 [================>.............] - ETA: 0s - loss: 3.6470e-05 - mean_absolute_error: 0.0044 96/126 [=====================>........] - ETA: 0s - loss: 3.5745e-05 - mean_absolute_error: 0.0044117/126 [==========================>...] - ETA: 0s - loss: 3.4630e-05 - mean_absolute_error: 0.0043126/126 [==============================] - 0s 3ms/step - loss: 3.4845e-05 - mean_absolute_error: 0.0043 - val_loss: 1.1928e-04 - val_mean_absolute_error: 0.0092
Epoch 92/100
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Epoch 93/100
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Epoch 94/100
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Epoch 95/100
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Epoch 96/100
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Epoch 97/100
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Epoch 98/100
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Epoch 99/100
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Epoch 100/100
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<keras.src.callbacks.History at 0x2115ad49a50>

After the training and fitting of the Machine Learning model for the United Kingdom, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for United Kingdom with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
united_kingdom_train_pred = united_kingdom_model.predict(X_united_kingdom_train).flatten()

plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4)
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Training Set Predictions vs. Observations")
plt.show()

united_kingdom_val_pred = united_kingdom_model.predict(X_united_kingdom_val).flatten()

plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Validation Set Predictions vs. Observations")
plt.show()

united_kingdom_test_pred = united_kingdom_model.predict(X_united_kingdom_test).flatten()

plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("United Kingdom - Testing Set Predictions vs. Observations")
plt.show()
```

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 1/16 [>.............................] - ETA: 0s16/16 [==============================] - 0s 2ms/step
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Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting the United Kingdom’s international currency rate with the United States.

```{python}
# Plotting United Kingdom's observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model) 
plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4)
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1)
plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4)
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1)
plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4)
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of United Kingdom's Dataset - Predictions vs. Observations")
plt.show()
```

Lastly, I worked on Switzerland’s data (as shown below):

Since the data (date, X, and y) is split into three np.arrays and to be more efficient, I will manually split Switzerland’s data into train, test, and validation datasets for the Machine Learning model with 80% going to the training dataset, the next 10% going to the validation dataset, and the last 10% going to the test dataset for each np.array respectively.

```{python}
# Splitting Switzerland's data into train, test, and validation sets on 3 
# mediums: the X-axis, the y-axis, and the indices (represented by dates)
dates_switzerland_train, X_switzerland_train, y_switzerland_train = dates_switzerland[:percentile_80], X_switzerland[:percentile_80], y_switzerland[:percentile_80]
dates_switzerland_val, X_switzerland_val, y_switzerland_val = dates_switzerland[percentile_80:percentile_90], X_switzerland[percentile_80:percentile_90], y_switzerland[percentile_80:percentile_90]
dates_switzerland_test, X_switzerland_test, y_switzerland_test = dates_switzerland[percentile_90:], X_switzerland[percentile_90:], y_switzerland[percentile_90:]

plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)

plt.legend(["Training", "Validation", "Testing"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Split of Switzerland's Data for LSTM Model")
plt.show()
```

Now, I began to configure the Machine Learning model. We added Sequential layers: an Input layer 3 by 1 because we will have 3 np.arrays of Input and 1 np.array as output, utilize a LSTM (Long Short-Term Memory) layer of 64 neurons, apply 2 levels of dense layers with 32 neurons and folliowing recommendations online to use the RELU (Rectified Linear Unit) Activiation Function, and I followed up with one last dense layer of 1 neuron as our output layer since we are just trying to linearly-predict the next currency-rate on a near-future date. Once I configured the Sequential layers, we are ready to compile the model, utilzing the mean_square_error as our minimizing loss function, using the Adam optimizer, and comparing our trained model against our data with the mean_absolute_error metric. Lastly, I fitted our model, utilzing our X_train and Y_train datasets for fitting with validation from our X_valid and Y_valid datasets at 100 epochs.

```{python}
# Configuring the Machine Learning Tensorflow Model for Switzerland
switzerland_model = Sequential([layers.Input((3, 1)),
                    layers.LSTM(64),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(32, activation="relu"),
                    layers.Dense(1)])

switzerland_model.compile(loss="mse",
              optimizer=Adam(learning_rate=0.001),
              metrics=["mean_absolute_error"])

switzerland_model.fit(X_switzerland_train, y_switzerland_train, validation_data=(X_switzerland_val, y_switzerland_val), epochs=100)
```

Epoch 1/100
  1/126 [..............................] - ETA: 3:42 - loss: 1.3730 - mean_absolute_error: 1.1415 21/126 [====>.........................] - ETA: 0s - loss: 0.7272 - mean_absolute_error: 0.7960   45/126 [=========>....................] - ETA: 0s - loss: 0.3596 - mean_absolute_error: 0.4608 70/126 [===============>..............] - ETA: 0s - loss: 0.2341 - mean_absolute_error: 0.3208 96/126 [=====================>........] - ETA: 0s - loss: 0.1720 - mean_absolute_error: 0.2496123/126 [============================>.] - ETA: 0s - loss: 0.1352 - mean_absolute_error: 0.2067126/126 [==============================] - 2s 6ms/step - loss: 0.1327 - mean_absolute_error: 0.2036 - val_loss: 0.0029 - val_mean_absolute_error: 0.0529
Epoch 2/100
  1/126 [..............................] - ETA: 0s - loss: 0.0042 - mean_absolute_error: 0.0552 21/126 [====>.........................] - ETA: 0s - loss: 0.0032 - mean_absolute_error: 0.0475 42/126 [=========>....................] - ETA: 0s - loss: 0.0028 - mean_absolute_error: 0.0438 66/126 [==============>...............] - ETA: 0s - loss: 0.0024 - mean_absolute_error: 0.0408 89/126 [====================>.........] - ETA: 0s - loss: 0.0022 - mean_absolute_error: 0.0381115/126 [==========================>...] - ETA: 0s - loss: 0.0019 - mean_absolute_error: 0.0355126/126 [==============================] - 0s 3ms/step - loss: 0.0018 - mean_absolute_error: 0.0344 - val_loss: 3.2377e-04 - val_mean_absolute_error: 0.0166
Epoch 3/100
  1/126 [..............................] - ETA: 0s - loss: 8.9574e-04 - mean_absolute_error: 0.0242 24/126 [====>.........................] - ETA: 0s - loss: 5.5201e-04 - mean_absolute_error: 0.0191 48/126 [==========>...................] - ETA: 0s - loss: 4.5932e-04 - mean_absolute_error: 0.0173 73/126 [================>.............] - ETA: 0s - loss: 4.0907e-04 - mean_absolute_error: 0.0162100/126 [======================>.......] - ETA: 0s - loss: 3.5389e-04 - mean_absolute_error: 0.0149126/126 [==============================] - ETA: 0s - loss: 3.2067e-04 - mean_absolute_error: 0.0140126/126 [==============================] - 0s 2ms/step - loss: 3.2067e-04 - mean_absolute_error: 0.0140 - val_loss: 8.5956e-05 - val_mean_absolute_error: 0.0076
Epoch 4/100
  1/126 [..............................] - ETA: 0s - loss: 1.1459e-04 - mean_absolute_error: 0.0092 24/126 [====>.........................] - ETA: 0s - loss: 1.5400e-04 - mean_absolute_error: 0.0099 49/126 [==========>...................] - ETA: 0s - loss: 1.4740e-04 - mean_absolute_error: 0.0096 73/126 [================>.............] - ETA: 0s - loss: 1.5037e-04 - mean_absolute_error: 0.0096 99/126 [======================>.......] - ETA: 0s - loss: 1.4593e-04 - mean_absolute_error: 0.0094125/126 [============================>.] - ETA: 0s - loss: 1.5039e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5047e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9823e-05 - val_mean_absolute_error: 0.0055
Epoch 5/100
  1/126 [..............................] - ETA: 0s - loss: 9.0082e-05 - mean_absolute_error: 0.0077 26/126 [=====>........................] - ETA: 0s - loss: 1.3687e-04 - mean_absolute_error: 0.0090 52/126 [===========>..................] - ETA: 0s - loss: 1.4424e-04 - mean_absolute_error: 0.0090 74/126 [================>.............] - ETA: 0s - loss: 1.4113e-04 - mean_absolute_error: 0.0089100/126 [======================>.......] - ETA: 0s - loss: 1.4091e-04 - mean_absolute_error: 0.0089123/126 [============================>.] - ETA: 0s - loss: 1.4480e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4554e-04 - mean_absolute_error: 0.0090 - val_loss: 6.5138e-05 - val_mean_absolute_error: 0.0065
Epoch 6/100
  1/126 [..............................] - ETA: 0s - loss: 6.4829e-04 - mean_absolute_error: 0.0134 26/126 [=====>........................] - ETA: 0s - loss: 1.5793e-04 - mean_absolute_error: 0.0092 53/126 [===========>..................] - ETA: 0s - loss: 1.4743e-04 - mean_absolute_error: 0.0091 79/126 [=================>............] - ETA: 0s - loss: 1.5552e-04 - mean_absolute_error: 0.0092106/126 [========================>.....] - ETA: 0s - loss: 1.5303e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5232e-04 - mean_absolute_error: 0.0093 - val_loss: 5.7060e-05 - val_mean_absolute_error: 0.0060
Epoch 7/100
  1/126 [..............................] - ETA: 0s - loss: 1.7864e-04 - mean_absolute_error: 0.0091 26/126 [=====>........................] - ETA: 0s - loss: 1.5495e-04 - mean_absolute_error: 0.0095 51/126 [===========>..................] - ETA: 0s - loss: 1.4610e-04 - mean_absolute_error: 0.0092 78/126 [=================>............] - ETA: 0s - loss: 1.4444e-04 - mean_absolute_error: 0.0092103/126 [=======================>......] - ETA: 0s - loss: 1.4189e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4971e-04 - mean_absolute_error: 0.0091 - val_loss: 4.9860e-05 - val_mean_absolute_error: 0.0055
Epoch 8/100
  1/126 [..............................] - ETA: 0s - loss: 1.8089e-04 - mean_absolute_error: 0.0104 24/126 [====>.........................] - ETA: 0s - loss: 1.4598e-04 - mean_absolute_error: 0.0091 51/126 [===========>..................] - ETA: 0s - loss: 1.4922e-04 - mean_absolute_error: 0.0090 78/126 [=================>............] - ETA: 0s - loss: 1.4662e-04 - mean_absolute_error: 0.0090104/126 [=======================>......] - ETA: 0s - loss: 1.4239e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4765e-04 - mean_absolute_error: 0.0090 - val_loss: 5.0121e-05 - val_mean_absolute_error: 0.0055
Epoch 9/100
  1/126 [..............................] - ETA: 0s - loss: 1.6461e-04 - mean_absolute_error: 0.0104 24/126 [====>.........................] - ETA: 0s - loss: 1.3712e-04 - mean_absolute_error: 0.0092 51/126 [===========>..................] - ETA: 0s - loss: 1.4779e-04 - mean_absolute_error: 0.0091 77/126 [=================>............] - ETA: 0s - loss: 1.4532e-04 - mean_absolute_error: 0.0090103/126 [=======================>......] - ETA: 0s - loss: 1.4642e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.5588e-04 - mean_absolute_error: 0.0093 - val_loss: 6.1764e-05 - val_mean_absolute_error: 0.0063
Epoch 10/100
  1/126 [..............................] - ETA: 0s - loss: 1.1407e-04 - mean_absolute_error: 0.0088 19/126 [===>..........................] - ETA: 0s - loss: 1.7799e-04 - mean_absolute_error: 0.0097 43/126 [=========>....................] - ETA: 0s - loss: 1.5900e-04 - mean_absolute_error: 0.0094 68/126 [===============>..............] - ETA: 0s - loss: 1.5524e-04 - mean_absolute_error: 0.0092 93/126 [=====================>........] - ETA: 0s - loss: 1.4911e-04 - mean_absolute_error: 0.0090119/126 [===========================>..] - ETA: 0s - loss: 1.5029e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5023e-04 - mean_absolute_error: 0.0092 - val_loss: 5.1470e-05 - val_mean_absolute_error: 0.0057
Epoch 11/100
  1/126 [..............................] - ETA: 0s - loss: 7.3217e-05 - mean_absolute_error: 0.0070 26/126 [=====>........................] - ETA: 0s - loss: 1.4848e-04 - mean_absolute_error: 0.0093 52/126 [===========>..................] - ETA: 0s - loss: 1.5310e-04 - mean_absolute_error: 0.0092 79/126 [=================>............] - ETA: 0s - loss: 1.5604e-04 - mean_absolute_error: 0.0092105/126 [========================>.....] - ETA: 0s - loss: 1.5055e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4906e-04 - mean_absolute_error: 0.0091 - val_loss: 5.6156e-05 - val_mean_absolute_error: 0.0060
Epoch 12/100
  1/126 [..............................] - ETA: 0s - loss: 1.1627e-04 - mean_absolute_error: 0.0089 20/126 [===>..........................] - ETA: 0s - loss: 1.7592e-04 - mean_absolute_error: 0.0097 42/126 [=========>....................] - ETA: 0s - loss: 1.5565e-04 - mean_absolute_error: 0.0093 66/126 [==============>...............] - ETA: 0s - loss: 1.5099e-04 - mean_absolute_error: 0.0090 91/126 [====================>.........] - ETA: 0s - loss: 1.4855e-04 - mean_absolute_error: 0.0090116/126 [==========================>...] - ETA: 0s - loss: 1.4725e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 3ms/step - loss: 1.4685e-04 - mean_absolute_error: 0.0090 - val_loss: 7.1898e-05 - val_mean_absolute_error: 0.0069
Epoch 13/100
  1/126 [..............................] - ETA: 0s - loss: 9.4426e-05 - mean_absolute_error: 0.0086 24/126 [====>.........................] - ETA: 0s - loss: 1.5994e-04 - mean_absolute_error: 0.0096 50/126 [==========>...................] - ETA: 0s - loss: 1.5668e-04 - mean_absolute_error: 0.0093 74/126 [================>.............] - ETA: 0s - loss: 1.5107e-04 - mean_absolute_error: 0.0092 88/126 [===================>..........] - ETA: 0s - loss: 1.6053e-04 - mean_absolute_error: 0.0094108/126 [========================>.....] - ETA: 0s - loss: 1.5505e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 3ms/step - loss: 1.5001e-04 - mean_absolute_error: 0.0092 - val_loss: 6.0124e-05 - val_mean_absolute_error: 0.0062
Epoch 14/100
  1/126 [..............................] - ETA: 0s - loss: 1.2783e-04 - mean_absolute_error: 0.0087 26/126 [=====>........................] - ETA: 0s - loss: 1.2019e-04 - mean_absolute_error: 0.0085 53/126 [===========>..................] - ETA: 0s - loss: 1.3768e-04 - mean_absolute_error: 0.0089 78/126 [=================>............] - ETA: 0s - loss: 1.4576e-04 - mean_absolute_error: 0.0090103/126 [=======================>......] - ETA: 0s - loss: 1.4744e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4752e-04 - mean_absolute_error: 0.0091 - val_loss: 5.0648e-05 - val_mean_absolute_error: 0.0056
Epoch 15/100
  1/126 [..............................] - ETA: 0s - loss: 1.3685e-04 - mean_absolute_error: 0.0087 22/126 [====>.........................] - ETA: 0s - loss: 1.2437e-04 - mean_absolute_error: 0.0086 47/126 [==========>...................] - ETA: 0s - loss: 1.3162e-04 - mean_absolute_error: 0.0087 71/126 [===============>..............] - ETA: 0s - loss: 1.4279e-04 - mean_absolute_error: 0.0089 97/126 [======================>.......] - ETA: 0s - loss: 1.4113e-04 - mean_absolute_error: 0.0089123/126 [============================>.] - ETA: 0s - loss: 1.4753e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4770e-04 - mean_absolute_error: 0.0090 - val_loss: 9.9290e-05 - val_mean_absolute_error: 0.0082
Epoch 16/100
  1/126 [..............................] - ETA: 0s - loss: 1.6259e-04 - mean_absolute_error: 0.0106 21/126 [====>.........................] - ETA: 0s - loss: 1.3525e-04 - mean_absolute_error: 0.0088 46/126 [=========>....................] - ETA: 0s - loss: 1.4180e-04 - mean_absolute_error: 0.0091 72/126 [================>.............] - ETA: 0s - loss: 1.3847e-04 - mean_absolute_error: 0.0090 97/126 [======================>.......] - ETA: 0s - loss: 1.4132e-04 - mean_absolute_error: 0.0089121/126 [===========================>..] - ETA: 0s - loss: 1.3973e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.4442e-04 - mean_absolute_error: 0.0089 - val_loss: 5.3399e-05 - val_mean_absolute_error: 0.0058
Epoch 17/100
  1/126 [..............................] - ETA: 0s - loss: 8.7346e-05 - mean_absolute_error: 0.0074 26/126 [=====>........................] - ETA: 0s - loss: 1.4165e-04 - mean_absolute_error: 0.0090 54/126 [===========>..................] - ETA: 0s - loss: 1.4448e-04 - mean_absolute_error: 0.0091 80/126 [==================>...........] - ETA: 0s - loss: 1.3783e-04 - mean_absolute_error: 0.0089106/126 [========================>.....] - ETA: 0s - loss: 1.4930e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4515e-04 - mean_absolute_error: 0.0090 - val_loss: 4.9412e-05 - val_mean_absolute_error: 0.0054
Epoch 18/100
  1/126 [..............................] - ETA: 0s - loss: 8.9229e-05 - mean_absolute_error: 0.0076 25/126 [====>.........................] - ETA: 0s - loss: 1.2348e-04 - mean_absolute_error: 0.0083 52/126 [===========>..................] - ETA: 0s - loss: 1.4205e-04 - mean_absolute_error: 0.0089 75/126 [================>.............] - ETA: 0s - loss: 1.5117e-04 - mean_absolute_error: 0.0092 98/126 [======================>.......] - ETA: 0s - loss: 1.4894e-04 - mean_absolute_error: 0.0092122/126 [============================>.] - ETA: 0s - loss: 1.5100e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5017e-04 - mean_absolute_error: 0.0091 - val_loss: 4.9542e-05 - val_mean_absolute_error: 0.0054
Epoch 19/100
  1/126 [..............................] - ETA: 0s - loss: 9.7513e-05 - mean_absolute_error: 0.0079 25/126 [====>.........................] - ETA: 0s - loss: 1.2849e-04 - mean_absolute_error: 0.0088 50/126 [==========>...................] - ETA: 0s - loss: 1.5306e-04 - mean_absolute_error: 0.0091 75/126 [================>.............] - ETA: 0s - loss: 1.5003e-04 - mean_absolute_error: 0.0089100/126 [======================>.......] - ETA: 0s - loss: 1.4608e-04 - mean_absolute_error: 0.0089125/126 [============================>.] - ETA: 0s - loss: 1.4221e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.4234e-04 - mean_absolute_error: 0.0089 - val_loss: 6.7265e-05 - val_mean_absolute_error: 0.0066
Epoch 20/100
  1/126 [..............................] - ETA: 0s - loss: 3.5723e-04 - mean_absolute_error: 0.0129 25/126 [====>.........................] - ETA: 0s - loss: 1.3492e-04 - mean_absolute_error: 0.0088 50/126 [==========>...................] - ETA: 0s - loss: 1.5718e-04 - mean_absolute_error: 0.0091 75/126 [================>.............] - ETA: 0s - loss: 1.6956e-04 - mean_absolute_error: 0.0096100/126 [======================>.......] - ETA: 0s - loss: 1.6584e-04 - mean_absolute_error: 0.0096125/126 [============================>.] - ETA: 0s - loss: 1.5989e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5989e-04 - mean_absolute_error: 0.0094 - val_loss: 4.9040e-05 - val_mean_absolute_error: 0.0054
Epoch 21/100
  1/126 [..............................] - ETA: 0s - loss: 1.5784e-04 - mean_absolute_error: 0.0097 25/126 [====>.........................] - ETA: 0s - loss: 1.2941e-04 - mean_absolute_error: 0.0087 49/126 [==========>...................] - ETA: 0s - loss: 1.2685e-04 - mean_absolute_error: 0.0086 71/126 [===============>..............] - ETA: 0s - loss: 1.4204e-04 - mean_absolute_error: 0.0088 90/126 [====================>.........] - ETA: 0s - loss: 1.4806e-04 - mean_absolute_error: 0.0090112/126 [=========================>....] - ETA: 0s - loss: 1.4662e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 3ms/step - loss: 1.4431e-04 - mean_absolute_error: 0.0090 - val_loss: 5.5608e-05 - val_mean_absolute_error: 0.0060
Epoch 22/100
  1/126 [..............................] - ETA: 0s - loss: 1.0242e-04 - mean_absolute_error: 0.0080 26/126 [=====>........................] - ETA: 0s - loss: 1.3343e-04 - mean_absolute_error: 0.0089 51/126 [===========>..................] - ETA: 0s - loss: 1.4565e-04 - mean_absolute_error: 0.0093 76/126 [=================>............] - ETA: 0s - loss: 1.6143e-04 - mean_absolute_error: 0.0094101/126 [=======================>......] - ETA: 0s - loss: 1.5706e-04 - mean_absolute_error: 0.0094125/126 [============================>.] - ETA: 0s - loss: 1.5111e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5108e-04 - mean_absolute_error: 0.0092 - val_loss: 5.0210e-05 - val_mean_absolute_error: 0.0055
Epoch 23/100
  1/126 [..............................] - ETA: 0s - loss: 7.3105e-05 - mean_absolute_error: 0.0069 26/126 [=====>........................] - ETA: 0s - loss: 1.5353e-04 - mean_absolute_error: 0.0093 51/126 [===========>..................] - ETA: 0s - loss: 1.4134e-04 - mean_absolute_error: 0.0091 76/126 [=================>............] - ETA: 0s - loss: 1.3714e-04 - mean_absolute_error: 0.0089101/126 [=======================>......] - ETA: 0s - loss: 1.4811e-04 - mean_absolute_error: 0.0090126/126 [==============================] - ETA: 0s - loss: 1.4760e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 2ms/step - loss: 1.4760e-04 - mean_absolute_error: 0.0091 - val_loss: 5.7673e-05 - val_mean_absolute_error: 0.0061
Epoch 24/100
  1/126 [..............................] - ETA: 0s - loss: 1.2537e-04 - mean_absolute_error: 0.0090 25/126 [====>.........................] - ETA: 0s - loss: 1.6627e-04 - mean_absolute_error: 0.0091 46/126 [=========>....................] - ETA: 0s - loss: 1.4451e-04 - mean_absolute_error: 0.0088 70/126 [===============>..............] - ETA: 0s - loss: 1.4147e-04 - mean_absolute_error: 0.0088 94/126 [=====================>........] - ETA: 0s - loss: 1.4086e-04 - mean_absolute_error: 0.0088120/126 [===========================>..] - ETA: 0s - loss: 1.4205e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.4534e-04 - mean_absolute_error: 0.0089 - val_loss: 5.3024e-05 - val_mean_absolute_error: 0.0058
Epoch 25/100
  1/126 [..............................] - ETA: 0s - loss: 1.9114e-04 - mean_absolute_error: 0.0100 23/126 [====>.........................] - ETA: 0s - loss: 1.3620e-04 - mean_absolute_error: 0.0086 47/126 [==========>...................] - ETA: 0s - loss: 1.3350e-04 - mean_absolute_error: 0.0086 72/126 [================>.............] - ETA: 0s - loss: 1.4330e-04 - mean_absolute_error: 0.0089 97/126 [======================>.......] - ETA: 0s - loss: 1.5044e-04 - mean_absolute_error: 0.0091122/126 [============================>.] - ETA: 0s - loss: 1.4408e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.4455e-04 - mean_absolute_error: 0.0090 - val_loss: 5.7125e-05 - val_mean_absolute_error: 0.0061
Epoch 26/100
  1/126 [..............................] - ETA: 0s - loss: 2.3450e-04 - mean_absolute_error: 0.0120 26/126 [=====>........................] - ETA: 0s - loss: 1.3909e-04 - mean_absolute_error: 0.0090 51/126 [===========>..................] - ETA: 0s - loss: 1.4903e-04 - mean_absolute_error: 0.0091 76/126 [=================>............] - ETA: 0s - loss: 1.4296e-04 - mean_absolute_error: 0.0090100/126 [======================>.......] - ETA: 0s - loss: 1.4584e-04 - mean_absolute_error: 0.0090124/126 [============================>.] - ETA: 0s - loss: 1.5245e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5229e-04 - mean_absolute_error: 0.0092 - val_loss: 7.3404e-05 - val_mean_absolute_error: 0.0070
Epoch 27/100
  1/126 [..............................] - ETA: 0s - loss: 1.4389e-04 - mean_absolute_error: 0.0095 26/126 [=====>........................] - ETA: 0s - loss: 1.5860e-04 - mean_absolute_error: 0.0097 50/126 [==========>...................] - ETA: 0s - loss: 1.7370e-04 - mean_absolute_error: 0.0099 74/126 [================>.............] - ETA: 0s - loss: 1.6543e-04 - mean_absolute_error: 0.0098 97/126 [======================>.......] - ETA: 0s - loss: 1.6447e-04 - mean_absolute_error: 0.0096122/126 [============================>.] - ETA: 0s - loss: 1.6263e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 2ms/step - loss: 1.6179e-04 - mean_absolute_error: 0.0096 - val_loss: 4.8947e-05 - val_mean_absolute_error: 0.0053
Epoch 28/100
  1/126 [..............................] - ETA: 0s - loss: 1.0686e-04 - mean_absolute_error: 0.0081 26/126 [=====>........................] - ETA: 0s - loss: 1.2665e-04 - mean_absolute_error: 0.0087 49/126 [==========>...................] - ETA: 0s - loss: 1.4139e-04 - mean_absolute_error: 0.0089 72/126 [================>.............] - ETA: 0s - loss: 1.4719e-04 - mean_absolute_error: 0.0090 97/126 [======================>.......] - ETA: 0s - loss: 1.5086e-04 - mean_absolute_error: 0.0091122/126 [============================>.] - ETA: 0s - loss: 1.5475e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5405e-04 - mean_absolute_error: 0.0093 - val_loss: 6.9488e-05 - val_mean_absolute_error: 0.0068
Epoch 29/100
  1/126 [..............................] - ETA: 0s - loss: 1.0316e-04 - mean_absolute_error: 0.0077 26/126 [=====>........................] - ETA: 0s - loss: 1.6152e-04 - mean_absolute_error: 0.0092 51/126 [===========>..................] - ETA: 0s - loss: 1.5706e-04 - mean_absolute_error: 0.0091 75/126 [================>.............] - ETA: 0s - loss: 1.6957e-04 - mean_absolute_error: 0.0097 99/126 [======================>.......] - ETA: 0s - loss: 1.6123e-04 - mean_absolute_error: 0.0095124/126 [============================>.] - ETA: 0s - loss: 1.5465e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5477e-04 - mean_absolute_error: 0.0093 - val_loss: 4.9112e-05 - val_mean_absolute_error: 0.0054
Epoch 30/100
  1/126 [..............................] - ETA: 0s - loss: 1.5564e-04 - mean_absolute_error: 0.0097 26/126 [=====>........................] - ETA: 0s - loss: 1.5072e-04 - mean_absolute_error: 0.0094 51/126 [===========>..................] - ETA: 0s - loss: 1.5435e-04 - mean_absolute_error: 0.0093 76/126 [=================>............] - ETA: 0s - loss: 1.5955e-04 - mean_absolute_error: 0.0095101/126 [=======================>......] - ETA: 0s - loss: 1.5639e-04 - mean_absolute_error: 0.0093123/126 [============================>.] - ETA: 0s - loss: 1.5123e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5194e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9153e-05 - val_mean_absolute_error: 0.0054
Epoch 31/100
  1/126 [..............................] - ETA: 0s - loss: 6.5165e-04 - mean_absolute_error: 0.0121 25/126 [====>.........................] - ETA: 0s - loss: 1.7427e-04 - mean_absolute_error: 0.0095 49/126 [==========>...................] - ETA: 0s - loss: 1.5060e-04 - mean_absolute_error: 0.0091 74/126 [================>.............] - ETA: 0s - loss: 1.4386e-04 - mean_absolute_error: 0.0091 97/126 [======================>.......] - ETA: 0s - loss: 1.4114e-04 - mean_absolute_error: 0.0090120/126 [===========================>..] - ETA: 0s - loss: 1.4537e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4509e-04 - mean_absolute_error: 0.0090 - val_loss: 4.9122e-05 - val_mean_absolute_error: 0.0054
Epoch 32/100
  1/126 [..............................] - ETA: 0s - loss: 1.1262e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.3695e-04 - mean_absolute_error: 0.0091 51/126 [===========>..................] - ETA: 0s - loss: 1.7179e-04 - mean_absolute_error: 0.0098 76/126 [=================>............] - ETA: 0s - loss: 1.6107e-04 - mean_absolute_error: 0.0095101/126 [=======================>......] - ETA: 0s - loss: 1.6018e-04 - mean_absolute_error: 0.0094125/126 [============================>.] - ETA: 0s - loss: 1.5656e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5685e-04 - mean_absolute_error: 0.0093 - val_loss: 5.0242e-05 - val_mean_absolute_error: 0.0056
Epoch 33/100
  1/126 [..............................] - ETA: 0s - loss: 1.3962e-04 - mean_absolute_error: 0.0091 26/126 [=====>........................] - ETA: 0s - loss: 1.4660e-04 - mean_absolute_error: 0.0095 49/126 [==========>...................] - ETA: 0s - loss: 1.5740e-04 - mean_absolute_error: 0.0099 74/126 [================>.............] - ETA: 0s - loss: 1.7527e-04 - mean_absolute_error: 0.0100 99/126 [======================>.......] - ETA: 0s - loss: 1.6892e-04 - mean_absolute_error: 0.0099124/126 [============================>.] - ETA: 0s - loss: 1.8012e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 2ms/step - loss: 1.7994e-04 - mean_absolute_error: 0.0102 - val_loss: 8.4862e-05 - val_mean_absolute_error: 0.0076
Epoch 34/100
  1/126 [..............................] - ETA: 0s - loss: 1.7046e-04 - mean_absolute_error: 0.0110 24/126 [====>.........................] - ETA: 0s - loss: 1.6121e-04 - mean_absolute_error: 0.0098 49/126 [==========>...................] - ETA: 0s - loss: 1.6938e-04 - mean_absolute_error: 0.0100 74/126 [================>.............] - ETA: 0s - loss: 1.7492e-04 - mean_absolute_error: 0.0100 99/126 [======================>.......] - ETA: 0s - loss: 1.7683e-04 - mean_absolute_error: 0.0100122/126 [============================>.] - ETA: 0s - loss: 1.7741e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.7906e-04 - mean_absolute_error: 0.0100 - val_loss: 1.2857e-04 - val_mean_absolute_error: 0.0097
Epoch 35/100
  1/126 [..............................] - ETA: 0s - loss: 3.1986e-04 - mean_absolute_error: 0.0143 26/126 [=====>........................] - ETA: 0s - loss: 1.5026e-04 - mean_absolute_error: 0.0095 51/126 [===========>..................] - ETA: 0s - loss: 1.4186e-04 - mean_absolute_error: 0.0092 76/126 [=================>............] - ETA: 0s - loss: 1.5518e-04 - mean_absolute_error: 0.0096101/126 [=======================>......] - ETA: 0s - loss: 1.5864e-04 - mean_absolute_error: 0.0096126/126 [==============================] - ETA: 0s - loss: 1.5821e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5821e-04 - mean_absolute_error: 0.0094 - val_loss: 7.3030e-05 - val_mean_absolute_error: 0.0070
Epoch 36/100
  1/126 [..............................] - ETA: 0s - loss: 1.1268e-04 - mean_absolute_error: 0.0084 25/126 [====>.........................] - ETA: 0s - loss: 1.3906e-04 - mean_absolute_error: 0.0091 50/126 [==========>...................] - ETA: 0s - loss: 1.3236e-04 - mean_absolute_error: 0.0088 74/126 [================>.............] - ETA: 0s - loss: 1.3844e-04 - mean_absolute_error: 0.0090 98/126 [======================>.......] - ETA: 0s - loss: 1.4934e-04 - mean_absolute_error: 0.0092123/126 [============================>.] - ETA: 0s - loss: 1.5217e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5196e-04 - mean_absolute_error: 0.0092 - val_loss: 6.1326e-05 - val_mean_absolute_error: 0.0063
Epoch 37/100
  1/126 [..............................] - ETA: 0s - loss: 1.0088e-04 - mean_absolute_error: 0.0080 25/126 [====>.........................] - ETA: 0s - loss: 1.6903e-04 - mean_absolute_error: 0.0090 48/126 [==========>...................] - ETA: 0s - loss: 1.5017e-04 - mean_absolute_error: 0.0088 72/126 [================>.............] - ETA: 0s - loss: 1.4332e-04 - mean_absolute_error: 0.0089 97/126 [======================>.......] - ETA: 0s - loss: 1.4880e-04 - mean_absolute_error: 0.0091122/126 [============================>.] - ETA: 0s - loss: 1.4977e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.5002e-04 - mean_absolute_error: 0.0092 - val_loss: 4.9157e-05 - val_mean_absolute_error: 0.0054
Epoch 38/100
  1/126 [..............................] - ETA: 0s - loss: 1.3328e-04 - mean_absolute_error: 0.0087 23/126 [====>.........................] - ETA: 0s - loss: 1.1986e-04 - mean_absolute_error: 0.0085 47/126 [==========>...................] - ETA: 0s - loss: 1.4391e-04 - mean_absolute_error: 0.0089 72/126 [================>.............] - ETA: 0s - loss: 1.6477e-04 - mean_absolute_error: 0.0097 97/126 [======================>.......] - ETA: 0s - loss: 1.7378e-04 - mean_absolute_error: 0.0099122/126 [============================>.] - ETA: 0s - loss: 1.7126e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.7210e-04 - mean_absolute_error: 0.0100 - val_loss: 1.6767e-04 - val_mean_absolute_error: 0.0112
Epoch 39/100
  1/126 [..............................] - ETA: 0s - loss: 3.1622e-04 - mean_absolute_error: 0.0153 25/126 [====>.........................] - ETA: 0s - loss: 2.4900e-04 - mean_absolute_error: 0.0124 50/126 [==========>...................] - ETA: 0s - loss: 2.0156e-04 - mean_absolute_error: 0.0106 74/126 [================>.............] - ETA: 0s - loss: 1.8487e-04 - mean_absolute_error: 0.0103 99/126 [======================>.......] - ETA: 0s - loss: 1.7190e-04 - mean_absolute_error: 0.0099122/126 [============================>.] - ETA: 0s - loss: 1.8304e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 3ms/step - loss: 1.8481e-04 - mean_absolute_error: 0.0103 - val_loss: 8.8153e-05 - val_mean_absolute_error: 0.0078
Epoch 40/100
  1/126 [..............................] - ETA: 0s - loss: 2.6380e-04 - mean_absolute_error: 0.0127 23/126 [====>.........................] - ETA: 0s - loss: 2.2519e-04 - mean_absolute_error: 0.0114 47/126 [==========>...................] - ETA: 0s - loss: 1.8744e-04 - mean_absolute_error: 0.0105 68/126 [===============>..............] - ETA: 0s - loss: 1.7860e-04 - mean_absolute_error: 0.0103 92/126 [====================>.........] - ETA: 0s - loss: 1.6901e-04 - mean_absolute_error: 0.0098117/126 [==========================>...] - ETA: 0s - loss: 1.6119e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 3ms/step - loss: 1.5820e-04 - mean_absolute_error: 0.0095 - val_loss: 4.8410e-05 - val_mean_absolute_error: 0.0053
Epoch 41/100
  1/126 [..............................] - ETA: 0s - loss: 1.2986e-04 - mean_absolute_error: 0.0081 24/126 [====>.........................] - ETA: 0s - loss: 1.7214e-04 - mean_absolute_error: 0.0101 48/126 [==========>...................] - ETA: 0s - loss: 1.7373e-04 - mean_absolute_error: 0.0102 73/126 [================>.............] - ETA: 0s - loss: 1.6277e-04 - mean_absolute_error: 0.0098 98/126 [======================>.......] - ETA: 0s - loss: 1.5861e-04 - mean_absolute_error: 0.0095121/126 [===========================>..] - ETA: 0s - loss: 1.6164e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 3ms/step - loss: 1.6114e-04 - mean_absolute_error: 0.0095 - val_loss: 9.4350e-05 - val_mean_absolute_error: 0.0080
Epoch 42/100
  1/126 [..............................] - ETA: 0s - loss: 1.3106e-04 - mean_absolute_error: 0.0095 24/126 [====>.........................] - ETA: 0s - loss: 1.4351e-04 - mean_absolute_error: 0.0087 48/126 [==========>...................] - ETA: 0s - loss: 1.4441e-04 - mean_absolute_error: 0.0087 72/126 [================>.............] - ETA: 0s - loss: 1.3733e-04 - mean_absolute_error: 0.0086 96/126 [=====================>........] - ETA: 0s - loss: 1.3341e-04 - mean_absolute_error: 0.0085121/126 [===========================>..] - ETA: 0s - loss: 1.3631e-04 - mean_absolute_error: 0.0086126/126 [==============================] - 0s 2ms/step - loss: 1.3568e-04 - mean_absolute_error: 0.0086 - val_loss: 1.0756e-04 - val_mean_absolute_error: 0.0086
Epoch 43/100
  1/126 [..............................] - ETA: 0s - loss: 2.4980e-04 - mean_absolute_error: 0.0135 26/126 [=====>........................] - ETA: 0s - loss: 1.5742e-04 - mean_absolute_error: 0.0092 51/126 [===========>..................] - ETA: 0s - loss: 1.6269e-04 - mean_absolute_error: 0.0096 75/126 [================>.............] - ETA: 0s - loss: 1.5452e-04 - mean_absolute_error: 0.0095 99/126 [======================>.......] - ETA: 0s - loss: 1.6213e-04 - mean_absolute_error: 0.0096122/126 [============================>.] - ETA: 0s - loss: 1.6192e-04 - mean_absolute_error: 0.0097126/126 [==============================] - 0s 3ms/step - loss: 1.6119e-04 - mean_absolute_error: 0.0096 - val_loss: 7.0193e-05 - val_mean_absolute_error: 0.0068
Epoch 44/100
  1/126 [..............................] - ETA: 0s - loss: 1.5904e-04 - mean_absolute_error: 0.0102 23/126 [====>.........................] - ETA: 0s - loss: 1.7126e-04 - mean_absolute_error: 0.0098 44/126 [=========>....................] - ETA: 0s - loss: 1.4892e-04 - mean_absolute_error: 0.0093 67/126 [==============>...............] - ETA: 0s - loss: 1.4308e-04 - mean_absolute_error: 0.0090 91/126 [====================>.........] - ETA: 0s - loss: 1.4229e-04 - mean_absolute_error: 0.0091115/126 [==========================>...] - ETA: 0s - loss: 1.4691e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 3ms/step - loss: 1.4819e-04 - mean_absolute_error: 0.0090 - val_loss: 4.8397e-05 - val_mean_absolute_error: 0.0054
Epoch 45/100
  1/126 [..............................] - ETA: 0s - loss: 1.9342e-04 - mean_absolute_error: 0.0106 25/126 [====>.........................] - ETA: 0s - loss: 1.6026e-04 - mean_absolute_error: 0.0091 50/126 [==========>...................] - ETA: 0s - loss: 1.5331e-04 - mean_absolute_error: 0.0092 75/126 [================>.............] - ETA: 0s - loss: 1.6190e-04 - mean_absolute_error: 0.0094100/126 [======================>.......] - ETA: 0s - loss: 1.5458e-04 - mean_absolute_error: 0.0092124/126 [============================>.] - ETA: 0s - loss: 1.5893e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5977e-04 - mean_absolute_error: 0.0095 - val_loss: 1.0604e-04 - val_mean_absolute_error: 0.0086
Epoch 46/100
  1/126 [..............................] - ETA: 0s - loss: 1.5434e-04 - mean_absolute_error: 0.0097 26/126 [=====>........................] - ETA: 0s - loss: 1.6480e-04 - mean_absolute_error: 0.0100 50/126 [==========>...................] - ETA: 0s - loss: 1.6618e-04 - mean_absolute_error: 0.0099 75/126 [================>.............] - ETA: 0s - loss: 1.5510e-04 - mean_absolute_error: 0.0094 97/126 [======================>.......] - ETA: 0s - loss: 1.5889e-04 - mean_absolute_error: 0.0095122/126 [============================>.] - ETA: 0s - loss: 1.6171e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5978e-04 - mean_absolute_error: 0.0095 - val_loss: 6.5169e-05 - val_mean_absolute_error: 0.0065
Epoch 47/100
  1/126 [..............................] - ETA: 0s - loss: 1.3930e-04 - mean_absolute_error: 0.0090 25/126 [====>.........................] - ETA: 0s - loss: 1.7541e-04 - mean_absolute_error: 0.0102 50/126 [==========>...................] - ETA: 0s - loss: 1.6952e-04 - mean_absolute_error: 0.0099 73/126 [================>.............] - ETA: 0s - loss: 1.8504e-04 - mean_absolute_error: 0.0105 97/126 [======================>.......] - ETA: 0s - loss: 1.8077e-04 - mean_absolute_error: 0.0104121/126 [===========================>..] - ETA: 0s - loss: 1.8789e-04 - mean_absolute_error: 0.0105126/126 [==============================] - 0s 2ms/step - loss: 1.8752e-04 - mean_absolute_error: 0.0105 - val_loss: 2.0233e-04 - val_mean_absolute_error: 0.0126
Epoch 48/100
  1/126 [..............................] - ETA: 0s - loss: 4.9602e-04 - mean_absolute_error: 0.0159 26/126 [=====>........................] - ETA: 0s - loss: 1.8180e-04 - mean_absolute_error: 0.0104 51/126 [===========>..................] - ETA: 0s - loss: 1.6265e-04 - mean_absolute_error: 0.0095 75/126 [================>.............] - ETA: 0s - loss: 1.6357e-04 - mean_absolute_error: 0.0097 99/126 [======================>.......] - ETA: 0s - loss: 1.6458e-04 - mean_absolute_error: 0.0097123/126 [============================>.] - ETA: 0s - loss: 1.7043e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.7007e-04 - mean_absolute_error: 0.0099 - val_loss: 1.1362e-04 - val_mean_absolute_error: 0.0090
Epoch 49/100
  1/126 [..............................] - ETA: 0s - loss: 1.8614e-04 - mean_absolute_error: 0.0105 26/126 [=====>........................] - ETA: 0s - loss: 1.8096e-04 - mean_absolute_error: 0.0107 51/126 [===========>..................] - ETA: 0s - loss: 1.6721e-04 - mean_absolute_error: 0.0100 77/126 [=================>............] - ETA: 0s - loss: 1.7034e-04 - mean_absolute_error: 0.0101101/126 [=======================>......] - ETA: 0s - loss: 1.7624e-04 - mean_absolute_error: 0.0100126/126 [==============================] - ETA: 0s - loss: 1.6715e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6715e-04 - mean_absolute_error: 0.0098 - val_loss: 4.7253e-05 - val_mean_absolute_error: 0.0052
Epoch 50/100
  1/126 [..............................] - ETA: 0s - loss: 7.8566e-05 - mean_absolute_error: 0.0061 26/126 [=====>........................] - ETA: 0s - loss: 1.4032e-04 - mean_absolute_error: 0.0090 50/126 [==========>...................] - ETA: 0s - loss: 1.3453e-04 - mean_absolute_error: 0.0088 75/126 [================>.............] - ETA: 0s - loss: 1.5355e-04 - mean_absolute_error: 0.0092 99/126 [======================>.......] - ETA: 0s - loss: 1.6347e-04 - mean_absolute_error: 0.0096122/126 [============================>.] - ETA: 0s - loss: 1.6219e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 2ms/step - loss: 1.6071e-04 - mean_absolute_error: 0.0096 - val_loss: 5.2550e-05 - val_mean_absolute_error: 0.0058
Epoch 51/100
  1/126 [..............................] - ETA: 0s - loss: 6.0766e-04 - mean_absolute_error: 0.0123 26/126 [=====>........................] - ETA: 0s - loss: 2.1561e-04 - mean_absolute_error: 0.0110 51/126 [===========>..................] - ETA: 0s - loss: 1.8405e-04 - mean_absolute_error: 0.0103 76/126 [=================>............] - ETA: 0s - loss: 1.9296e-04 - mean_absolute_error: 0.0105101/126 [=======================>......] - ETA: 0s - loss: 1.8044e-04 - mean_absolute_error: 0.0102126/126 [==============================] - ETA: 0s - loss: 1.6994e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6994e-04 - mean_absolute_error: 0.0099 - val_loss: 5.0473e-05 - val_mean_absolute_error: 0.0056
Epoch 52/100
  1/126 [..............................] - ETA: 0s - loss: 6.1130e-05 - mean_absolute_error: 0.0063 25/126 [====>.........................] - ETA: 0s - loss: 1.2243e-04 - mean_absolute_error: 0.0083 51/126 [===========>..................] - ETA: 0s - loss: 1.7698e-04 - mean_absolute_error: 0.0098 76/126 [=================>............] - ETA: 0s - loss: 1.6688e-04 - mean_absolute_error: 0.0097100/126 [======================>.......] - ETA: 0s - loss: 1.6280e-04 - mean_absolute_error: 0.0095125/126 [============================>.] - ETA: 0s - loss: 1.5741e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5725e-04 - mean_absolute_error: 0.0094 - val_loss: 7.2022e-05 - val_mean_absolute_error: 0.0070
Epoch 53/100
  1/126 [..............................] - ETA: 0s - loss: 8.2797e-05 - mean_absolute_error: 0.0071 23/126 [====>.........................] - ETA: 0s - loss: 1.3602e-04 - mean_absolute_error: 0.0082 48/126 [==========>...................] - ETA: 0s - loss: 1.7856e-04 - mean_absolute_error: 0.0098 73/126 [================>.............] - ETA: 0s - loss: 1.7225e-04 - mean_absolute_error: 0.0098 98/126 [======================>.......] - ETA: 0s - loss: 1.6574e-04 - mean_absolute_error: 0.0096122/126 [============================>.] - ETA: 0s - loss: 1.5528e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5544e-04 - mean_absolute_error: 0.0093 - val_loss: 5.6429e-05 - val_mean_absolute_error: 0.0060
Epoch 54/100
  1/126 [..............................] - ETA: 0s - loss: 1.6210e-04 - mean_absolute_error: 0.0094 25/126 [====>.........................] - ETA: 0s - loss: 1.3138e-04 - mean_absolute_error: 0.0087 50/126 [==========>...................] - ETA: 0s - loss: 1.3652e-04 - mean_absolute_error: 0.0089 75/126 [================>.............] - ETA: 0s - loss: 1.3282e-04 - mean_absolute_error: 0.0088100/126 [======================>.......] - ETA: 0s - loss: 1.5066e-04 - mean_absolute_error: 0.0092124/126 [============================>.] - ETA: 0s - loss: 1.4906e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4971e-04 - mean_absolute_error: 0.0092 - val_loss: 8.4331e-05 - val_mean_absolute_error: 0.0076
Epoch 55/100
  1/126 [..............................] - ETA: 0s - loss: 9.7171e-05 - mean_absolute_error: 0.0082 26/126 [=====>........................] - ETA: 0s - loss: 1.3163e-04 - mean_absolute_error: 0.0088 50/126 [==========>...................] - ETA: 0s - loss: 1.2457e-04 - mean_absolute_error: 0.0086 75/126 [================>.............] - ETA: 0s - loss: 1.3303e-04 - mean_absolute_error: 0.0089 99/126 [======================>.......] - ETA: 0s - loss: 1.4135e-04 - mean_absolute_error: 0.0089123/126 [============================>.] - ETA: 0s - loss: 1.4571e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4529e-04 - mean_absolute_error: 0.0090 - val_loss: 4.7296e-05 - val_mean_absolute_error: 0.0054
Epoch 56/100
  1/126 [..............................] - ETA: 0s - loss: 2.4613e-04 - mean_absolute_error: 0.0119 25/126 [====>.........................] - ETA: 0s - loss: 2.0530e-04 - mean_absolute_error: 0.0102 49/126 [==========>...................] - ETA: 0s - loss: 1.8029e-04 - mean_absolute_error: 0.0100 71/126 [===============>..............] - ETA: 0s - loss: 1.6228e-04 - mean_absolute_error: 0.0095 96/126 [=====================>........] - ETA: 0s - loss: 1.5363e-04 - mean_absolute_error: 0.0092121/126 [===========================>..] - ETA: 0s - loss: 1.5402e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5498e-04 - mean_absolute_error: 0.0094 - val_loss: 8.2041e-05 - val_mean_absolute_error: 0.0075
Epoch 57/100
  1/126 [..............................] - ETA: 0s - loss: 1.2733e-04 - mean_absolute_error: 0.0094 25/126 [====>.........................] - ETA: 0s - loss: 1.4638e-04 - mean_absolute_error: 0.0095 49/126 [==========>...................] - ETA: 0s - loss: 1.5747e-04 - mean_absolute_error: 0.0095 72/126 [================>.............] - ETA: 0s - loss: 1.6059e-04 - mean_absolute_error: 0.0095 97/126 [======================>.......] - ETA: 0s - loss: 1.6136e-04 - mean_absolute_error: 0.0096121/126 [===========================>..] - ETA: 0s - loss: 1.5573e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5464e-04 - mean_absolute_error: 0.0094 - val_loss: 4.6497e-05 - val_mean_absolute_error: 0.0052
Epoch 58/100
  1/126 [..............................] - ETA: 0s - loss: 1.1102e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.6909e-04 - mean_absolute_error: 0.0095 51/126 [===========>..................] - ETA: 0s - loss: 1.6254e-04 - mean_absolute_error: 0.0096 76/126 [=================>............] - ETA: 0s - loss: 1.6510e-04 - mean_absolute_error: 0.0096101/126 [=======================>......] - ETA: 0s - loss: 1.6799e-04 - mean_absolute_error: 0.0099126/126 [==============================] - ETA: 0s - loss: 1.6213e-04 - mean_absolute_error: 0.0097126/126 [==============================] - 0s 2ms/step - loss: 1.6213e-04 - mean_absolute_error: 0.0097 - val_loss: 5.0336e-05 - val_mean_absolute_error: 0.0056
Epoch 59/100
  1/126 [..............................] - ETA: 0s - loss: 1.9284e-04 - mean_absolute_error: 0.0098 25/126 [====>.........................] - ETA: 0s - loss: 1.3278e-04 - mean_absolute_error: 0.0087 51/126 [===========>..................] - ETA: 0s - loss: 1.4291e-04 - mean_absolute_error: 0.0089 76/126 [=================>............] - ETA: 0s - loss: 1.4920e-04 - mean_absolute_error: 0.0093 98/126 [======================>.......] - ETA: 0s - loss: 1.5952e-04 - mean_absolute_error: 0.0095122/126 [============================>.] - ETA: 0s - loss: 1.6519e-04 - mean_absolute_error: 0.0097126/126 [==============================] - 0s 2ms/step - loss: 1.6550e-04 - mean_absolute_error: 0.0097 - val_loss: 4.6445e-05 - val_mean_absolute_error: 0.0052
Epoch 60/100
  1/126 [..............................] - ETA: 0s - loss: 1.0574e-04 - mean_absolute_error: 0.0082 25/126 [====>.........................] - ETA: 0s - loss: 1.2315e-04 - mean_absolute_error: 0.0085 50/126 [==========>...................] - ETA: 0s - loss: 1.3659e-04 - mean_absolute_error: 0.0090 72/126 [================>.............] - ETA: 0s - loss: 1.5027e-04 - mean_absolute_error: 0.0093 97/126 [======================>.......] - ETA: 0s - loss: 1.4677e-04 - mean_absolute_error: 0.0092121/126 [===========================>..] - ETA: 0s - loss: 1.5302e-04 - mean_absolute_error: 0.0093126/126 [==============================] - 0s 2ms/step - loss: 1.5261e-04 - mean_absolute_error: 0.0093 - val_loss: 4.6395e-05 - val_mean_absolute_error: 0.0052
Epoch 61/100
  1/126 [..............................] - ETA: 0s - loss: 7.3138e-05 - mean_absolute_error: 0.0065 26/126 [=====>........................] - ETA: 0s - loss: 1.2791e-04 - mean_absolute_error: 0.0086 51/126 [===========>..................] - ETA: 0s - loss: 1.4870e-04 - mean_absolute_error: 0.0091 76/126 [=================>............] - ETA: 0s - loss: 1.5255e-04 - mean_absolute_error: 0.0093100/126 [======================>.......] - ETA: 0s - loss: 1.6484e-04 - mean_absolute_error: 0.0098124/126 [============================>.] - ETA: 0s - loss: 1.6871e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6795e-04 - mean_absolute_error: 0.0098 - val_loss: 5.3717e-05 - val_mean_absolute_error: 0.0058
Epoch 62/100
  1/126 [..............................] - ETA: 0s - loss: 9.8585e-05 - mean_absolute_error: 0.0077 25/126 [====>.........................] - ETA: 0s - loss: 1.2411e-04 - mean_absolute_error: 0.0086 50/126 [==========>...................] - ETA: 0s - loss: 1.2788e-04 - mean_absolute_error: 0.0087 76/126 [=================>............] - ETA: 0s - loss: 1.3378e-04 - mean_absolute_error: 0.0087101/126 [=======================>......] - ETA: 0s - loss: 1.4004e-04 - mean_absolute_error: 0.0088125/126 [============================>.] - ETA: 0s - loss: 1.4362e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4438e-04 - mean_absolute_error: 0.0090 - val_loss: 9.5082e-05 - val_mean_absolute_error: 0.0081
Epoch 63/100
  1/126 [..............................] - ETA: 0s - loss: 1.4934e-04 - mean_absolute_error: 0.0103 26/126 [=====>........................] - ETA: 0s - loss: 1.8711e-04 - mean_absolute_error: 0.0100 51/126 [===========>..................] - ETA: 0s - loss: 1.5205e-04 - mean_absolute_error: 0.0092 76/126 [=================>............] - ETA: 0s - loss: 1.7781e-04 - mean_absolute_error: 0.0102 99/126 [======================>.......] - ETA: 0s - loss: 1.7406e-04 - mean_absolute_error: 0.0099123/126 [============================>.] - ETA: 0s - loss: 1.6615e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6469e-04 - mean_absolute_error: 0.0097 - val_loss: 5.0526e-05 - val_mean_absolute_error: 0.0056
Epoch 64/100
  1/126 [..............................] - ETA: 0s - loss: 7.9829e-05 - mean_absolute_error: 0.0071 25/126 [====>.........................] - ETA: 0s - loss: 1.3992e-04 - mean_absolute_error: 0.0091 50/126 [==========>...................] - ETA: 0s - loss: 1.8274e-04 - mean_absolute_error: 0.0103 75/126 [================>.............] - ETA: 0s - loss: 1.8735e-04 - mean_absolute_error: 0.0105100/126 [======================>.......] - ETA: 0s - loss: 1.8606e-04 - mean_absolute_error: 0.0105125/126 [============================>.] - ETA: 0s - loss: 1.7696e-04 - mean_absolute_error: 0.0102126/126 [==============================] - 0s 2ms/step - loss: 1.7671e-04 - mean_absolute_error: 0.0102 - val_loss: 4.5935e-05 - val_mean_absolute_error: 0.0052
Epoch 65/100
  1/126 [..............................] - ETA: 0s - loss: 1.2449e-04 - mean_absolute_error: 0.0084 26/126 [=====>........................] - ETA: 0s - loss: 1.1696e-04 - mean_absolute_error: 0.0082 50/126 [==========>...................] - ETA: 0s - loss: 1.2973e-04 - mean_absolute_error: 0.0084 75/126 [================>.............] - ETA: 0s - loss: 1.3318e-04 - mean_absolute_error: 0.0086100/126 [======================>.......] - ETA: 0s - loss: 1.3274e-04 - mean_absolute_error: 0.0087125/126 [============================>.] - ETA: 0s - loss: 1.3648e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 2ms/step - loss: 1.3628e-04 - mean_absolute_error: 0.0087 - val_loss: 7.2885e-05 - val_mean_absolute_error: 0.0070
Epoch 66/100
  1/126 [..............................] - ETA: 0s - loss: 9.8194e-05 - mean_absolute_error: 0.0080 24/126 [====>.........................] - ETA: 0s - loss: 1.2653e-04 - mean_absolute_error: 0.0087 48/126 [==========>...................] - ETA: 0s - loss: 1.2036e-04 - mean_absolute_error: 0.0083 72/126 [================>.............] - ETA: 0s - loss: 1.3526e-04 - mean_absolute_error: 0.0086 96/126 [=====================>........] - ETA: 0s - loss: 1.3955e-04 - mean_absolute_error: 0.0087121/126 [===========================>..] - ETA: 0s - loss: 1.3525e-04 - mean_absolute_error: 0.0086126/126 [==============================] - 0s 2ms/step - loss: 1.3484e-04 - mean_absolute_error: 0.0086 - val_loss: 4.6705e-05 - val_mean_absolute_error: 0.0053
Epoch 67/100
  1/126 [..............................] - ETA: 0s - loss: 1.3006e-04 - mean_absolute_error: 0.0088 25/126 [====>.........................] - ETA: 0s - loss: 1.4334e-04 - mean_absolute_error: 0.0093 50/126 [==========>...................] - ETA: 0s - loss: 1.4531e-04 - mean_absolute_error: 0.0092 75/126 [================>.............] - ETA: 0s - loss: 1.4573e-04 - mean_absolute_error: 0.0092100/126 [======================>.......] - ETA: 0s - loss: 1.3953e-04 - mean_absolute_error: 0.0090125/126 [============================>.] - ETA: 0s - loss: 1.4862e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4864e-04 - mean_absolute_error: 0.0092 - val_loss: 9.3936e-05 - val_mean_absolute_error: 0.0081
Epoch 68/100
  1/126 [..............................] - ETA: 0s - loss: 1.6811e-04 - mean_absolute_error: 0.0105 26/126 [=====>........................] - ETA: 0s - loss: 1.5639e-04 - mean_absolute_error: 0.0098 52/126 [===========>..................] - ETA: 0s - loss: 1.6296e-04 - mean_absolute_error: 0.0100 76/126 [=================>............] - ETA: 0s - loss: 1.5628e-04 - mean_absolute_error: 0.0097101/126 [=======================>......] - ETA: 0s - loss: 1.6203e-04 - mean_absolute_error: 0.0097126/126 [==============================] - ETA: 0s - loss: 1.5753e-04 - mean_absolute_error: 0.0095126/126 [==============================] - 0s 2ms/step - loss: 1.5753e-04 - mean_absolute_error: 0.0095 - val_loss: 5.0655e-05 - val_mean_absolute_error: 0.0056
Epoch 69/100
  1/126 [..............................] - ETA: 0s - loss: 1.1754e-04 - mean_absolute_error: 0.0082 26/126 [=====>........................] - ETA: 0s - loss: 1.0694e-04 - mean_absolute_error: 0.0079 49/126 [==========>...................] - ETA: 0s - loss: 1.3712e-04 - mean_absolute_error: 0.0088 72/126 [================>.............] - ETA: 0s - loss: 1.3455e-04 - mean_absolute_error: 0.0087 96/126 [=====================>........] - ETA: 0s - loss: 1.4103e-04 - mean_absolute_error: 0.0089119/126 [===========================>..] - ETA: 0s - loss: 1.3464e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 3ms/step - loss: 1.3468e-04 - mean_absolute_error: 0.0087 - val_loss: 6.3122e-05 - val_mean_absolute_error: 0.0065
Epoch 70/100
  1/126 [..............................] - ETA: 0s - loss: 1.6454e-04 - mean_absolute_error: 0.0100 25/126 [====>.........................] - ETA: 0s - loss: 1.2374e-04 - mean_absolute_error: 0.0087 48/126 [==========>...................] - ETA: 0s - loss: 1.3146e-04 - mean_absolute_error: 0.0089 72/126 [================>.............] - ETA: 0s - loss: 1.4341e-04 - mean_absolute_error: 0.0090 97/126 [======================>.......] - ETA: 0s - loss: 1.3736e-04 - mean_absolute_error: 0.0088121/126 [===========================>..] - ETA: 0s - loss: 1.3974e-04 - mean_absolute_error: 0.0089126/126 [==============================] - 0s 2ms/step - loss: 1.3930e-04 - mean_absolute_error: 0.0089 - val_loss: 5.0316e-05 - val_mean_absolute_error: 0.0056
Epoch 71/100
  1/126 [..............................] - ETA: 0s - loss: 9.4252e-05 - mean_absolute_error: 0.0071 26/126 [=====>........................] - ETA: 0s - loss: 1.4291e-04 - mean_absolute_error: 0.0095 51/126 [===========>..................] - ETA: 0s - loss: 1.6993e-04 - mean_absolute_error: 0.0100 77/126 [=================>............] - ETA: 0s - loss: 1.4865e-04 - mean_absolute_error: 0.0093102/126 [=======================>......] - ETA: 0s - loss: 1.5463e-04 - mean_absolute_error: 0.0096126/126 [==============================] - 0s 2ms/step - loss: 1.5849e-04 - mean_absolute_error: 0.0096 - val_loss: 5.4975e-05 - val_mean_absolute_error: 0.0059
Epoch 72/100
  1/126 [..............................] - ETA: 0s - loss: 1.6807e-04 - mean_absolute_error: 0.0107 27/126 [=====>........................] - ETA: 0s - loss: 1.4581e-04 - mean_absolute_error: 0.0090 52/126 [===========>..................] - ETA: 0s - loss: 1.3798e-04 - mean_absolute_error: 0.0088 76/126 [=================>............] - ETA: 0s - loss: 1.3249e-04 - mean_absolute_error: 0.0087 99/126 [======================>.......] - ETA: 0s - loss: 1.4410e-04 - mean_absolute_error: 0.0090123/126 [============================>.] - ETA: 0s - loss: 1.4867e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4907e-04 - mean_absolute_error: 0.0092 - val_loss: 1.6004e-04 - val_mean_absolute_error: 0.0111
Epoch 73/100
  1/126 [..............................] - ETA: 0s - loss: 1.7092e-04 - mean_absolute_error: 0.0114 25/126 [====>.........................] - ETA: 0s - loss: 1.6568e-04 - mean_absolute_error: 0.0100 50/126 [==========>...................] - ETA: 0s - loss: 1.5901e-04 - mean_absolute_error: 0.0094 73/126 [================>.............] - ETA: 0s - loss: 1.4645e-04 - mean_absolute_error: 0.0091 98/126 [======================>.......] - ETA: 0s - loss: 1.5060e-04 - mean_absolute_error: 0.0092123/126 [============================>.] - ETA: 0s - loss: 1.6768e-04 - mean_absolute_error: 0.0098126/126 [==============================] - 0s 2ms/step - loss: 1.6906e-04 - mean_absolute_error: 0.0098 - val_loss: 1.2974e-04 - val_mean_absolute_error: 0.0097
Epoch 74/100
  1/126 [..............................] - ETA: 0s - loss: 1.8586e-04 - mean_absolute_error: 0.0116 25/126 [====>.........................] - ETA: 0s - loss: 1.4430e-04 - mean_absolute_error: 0.0095 49/126 [==========>...................] - ETA: 0s - loss: 1.5808e-04 - mean_absolute_error: 0.0099 75/126 [================>.............] - ETA: 0s - loss: 1.5162e-04 - mean_absolute_error: 0.0095 99/126 [======================>.......] - ETA: 0s - loss: 1.4073e-04 - mean_absolute_error: 0.0091122/126 [============================>.] - ETA: 0s - loss: 1.4775e-04 - mean_absolute_error: 0.0091126/126 [==============================] - 0s 3ms/step - loss: 1.5049e-04 - mean_absolute_error: 0.0092 - val_loss: 4.4333e-05 - val_mean_absolute_error: 0.0050
Epoch 75/100
  1/126 [..............................] - ETA: 0s - loss: 7.5629e-05 - mean_absolute_error: 0.0063 25/126 [====>.........................] - ETA: 0s - loss: 1.4044e-04 - mean_absolute_error: 0.0092 50/126 [==========>...................] - ETA: 0s - loss: 1.7755e-04 - mean_absolute_error: 0.0102 74/126 [================>.............] - ETA: 0s - loss: 1.8762e-04 - mean_absolute_error: 0.0104 98/126 [======================>.......] - ETA: 0s - loss: 1.7189e-04 - mean_absolute_error: 0.0100120/126 [===========================>..] - ETA: 0s - loss: 1.6841e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6651e-04 - mean_absolute_error: 0.0098 - val_loss: 6.9119e-05 - val_mean_absolute_error: 0.0068
Epoch 76/100
  1/126 [..............................] - ETA: 0s - loss: 1.1221e-04 - mean_absolute_error: 0.0078 25/126 [====>.........................] - ETA: 0s - loss: 1.2426e-04 - mean_absolute_error: 0.0086 50/126 [==========>...................] - ETA: 0s - loss: 1.1464e-04 - mean_absolute_error: 0.0082 74/126 [================>.............] - ETA: 0s - loss: 1.4280e-04 - mean_absolute_error: 0.0090 96/126 [=====================>........] - ETA: 0s - loss: 1.6255e-04 - mean_absolute_error: 0.0096120/126 [===========================>..] - ETA: 0s - loss: 1.7447e-04 - mean_absolute_error: 0.0101126/126 [==============================] - 0s 2ms/step - loss: 1.7807e-04 - mean_absolute_error: 0.0102 - val_loss: 6.7277e-05 - val_mean_absolute_error: 0.0067
Epoch 77/100
  1/126 [..............................] - ETA: 0s - loss: 1.0839e-04 - mean_absolute_error: 0.0093 26/126 [=====>........................] - ETA: 0s - loss: 1.1881e-04 - mean_absolute_error: 0.0084 51/126 [===========>..................] - ETA: 0s - loss: 1.2983e-04 - mean_absolute_error: 0.0089 76/126 [=================>............] - ETA: 0s - loss: 1.2436e-04 - mean_absolute_error: 0.0086101/126 [=======================>......] - ETA: 0s - loss: 1.3154e-04 - mean_absolute_error: 0.0086126/126 [==============================] - ETA: 0s - loss: 1.3183e-04 - mean_absolute_error: 0.0086126/126 [==============================] - 0s 2ms/step - loss: 1.3183e-04 - mean_absolute_error: 0.0086 - val_loss: 4.4626e-05 - val_mean_absolute_error: 0.0051
Epoch 78/100
  1/126 [..............................] - ETA: 0s - loss: 1.0196e-04 - mean_absolute_error: 0.0079 26/126 [=====>........................] - ETA: 0s - loss: 1.3343e-04 - mean_absolute_error: 0.0090 51/126 [===========>..................] - ETA: 0s - loss: 1.4248e-04 - mean_absolute_error: 0.0089 76/126 [=================>............] - ETA: 0s - loss: 1.3843e-04 - mean_absolute_error: 0.0089101/126 [=======================>......] - ETA: 0s - loss: 1.4016e-04 - mean_absolute_error: 0.0089126/126 [==============================] - ETA: 0s - loss: 1.3683e-04 - mean_absolute_error: 0.0088126/126 [==============================] - 0s 2ms/step - loss: 1.3683e-04 - mean_absolute_error: 0.0088 - val_loss: 4.8424e-05 - val_mean_absolute_error: 0.0055
Epoch 79/100
  1/126 [..............................] - ETA: 0s - loss: 8.6225e-05 - mean_absolute_error: 0.0069 23/126 [====>.........................] - ETA: 0s - loss: 1.3151e-04 - mean_absolute_error: 0.0088 47/126 [==========>...................] - ETA: 0s - loss: 1.3279e-04 - mean_absolute_error: 0.0088 72/126 [================>.............] - ETA: 0s - loss: 1.3465e-04 - mean_absolute_error: 0.0089 96/126 [=====================>........] - ETA: 0s - loss: 1.3248e-04 - mean_absolute_error: 0.0086120/126 [===========================>..] - ETA: 0s - loss: 1.3582e-04 - mean_absolute_error: 0.0087126/126 [==============================] - 0s 3ms/step - loss: 1.3785e-04 - mean_absolute_error: 0.0088 - val_loss: 6.2146e-05 - val_mean_absolute_error: 0.0064
Epoch 80/100
  1/126 [..............................] - ETA: 0s - loss: 1.2237e-04 - mean_absolute_error: 0.0089 26/126 [=====>........................] - ETA: 0s - loss: 1.5680e-04 - mean_absolute_error: 0.0094 51/126 [===========>..................] - ETA: 0s - loss: 1.7965e-04 - mean_absolute_error: 0.0100 75/126 [================>.............] - ETA: 0s - loss: 1.7191e-04 - mean_absolute_error: 0.0100 99/126 [======================>.......] - ETA: 0s - loss: 1.6005e-04 - mean_absolute_error: 0.0096124/126 [============================>.] - ETA: 0s - loss: 1.5137e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5151e-04 - mean_absolute_error: 0.0094 - val_loss: 4.3927e-05 - val_mean_absolute_error: 0.0050
Epoch 81/100
  1/126 [..............................] - ETA: 0s - loss: 1.0510e-04 - mean_absolute_error: 0.0087 25/126 [====>.........................] - ETA: 0s - loss: 1.0251e-04 - mean_absolute_error: 0.0079 50/126 [==========>...................] - ETA: 0s - loss: 1.0811e-04 - mean_absolute_error: 0.0080 74/126 [================>.............] - ETA: 0s - loss: 1.2380e-04 - mean_absolute_error: 0.0083 99/126 [======================>.......] - ETA: 0s - loss: 1.3421e-04 - mean_absolute_error: 0.0086124/126 [============================>.] - ETA: 0s - loss: 1.3253e-04 - mean_absolute_error: 0.0086126/126 [==============================] - 0s 2ms/step - loss: 1.3347e-04 - mean_absolute_error: 0.0087 - val_loss: 8.7438e-05 - val_mean_absolute_error: 0.0078
Epoch 82/100
  1/126 [..............................] - ETA: 0s - loss: 1.3322e-04 - mean_absolute_error: 0.0096 26/126 [=====>........................] - ETA: 0s - loss: 1.7287e-04 - mean_absolute_error: 0.0100 49/126 [==========>...................] - ETA: 0s - loss: 1.6560e-04 - mean_absolute_error: 0.0100 73/126 [================>.............] - ETA: 0s - loss: 1.6269e-04 - mean_absolute_error: 0.0098 98/126 [======================>.......] - ETA: 0s - loss: 1.6632e-04 - mean_absolute_error: 0.0100123/126 [============================>.] - ETA: 0s - loss: 1.6589e-04 - mean_absolute_error: 0.0099126/126 [==============================] - 0s 2ms/step - loss: 1.6567e-04 - mean_absolute_error: 0.0099 - val_loss: 9.9430e-05 - val_mean_absolute_error: 0.0084
Epoch 83/100
  1/126 [..............................] - ETA: 0s - loss: 1.3170e-04 - mean_absolute_error: 0.0086 23/126 [====>.........................] - ETA: 0s - loss: 1.8048e-04 - mean_absolute_error: 0.0105 47/126 [==========>...................] - ETA: 0s - loss: 1.5713e-04 - mean_absolute_error: 0.0097 72/126 [================>.............] - ETA: 0s - loss: 1.6198e-04 - mean_absolute_error: 0.0094 97/126 [======================>.......] - ETA: 0s - loss: 1.5867e-04 - mean_absolute_error: 0.0094122/126 [============================>.] - ETA: 0s - loss: 1.5421e-04 - mean_absolute_error: 0.0094126/126 [==============================] - 0s 2ms/step - loss: 1.5378e-04 - mean_absolute_error: 0.0094 - val_loss: 4.4092e-05 - val_mean_absolute_error: 0.0051
Epoch 84/100
  1/126 [..............................] - ETA: 0s - loss: 1.3963e-04 - mean_absolute_error: 0.0084 25/126 [====>.........................] - ETA: 0s - loss: 1.2916e-04 - mean_absolute_error: 0.0086 50/126 [==========>...................] - ETA: 0s - loss: 1.4200e-04 - mean_absolute_error: 0.0090 75/126 [================>.............] - ETA: 0s - loss: 1.5302e-04 - mean_absolute_error: 0.0092100/126 [======================>.......] - ETA: 0s - loss: 1.4495e-04 - mean_absolute_error: 0.0090125/126 [============================>.] - ETA: 0s - loss: 1.4683e-04 - mean_absolute_error: 0.0092126/126 [==============================] - 0s 2ms/step - loss: 1.4665e-04 - mean_absolute_error: 0.0092 - val_loss: 1.5284e-04 - val_mean_absolute_error: 0.0107
Epoch 85/100
  1/126 [..............................] - ETA: 0s - loss: 3.6833e-04 - mean_absolute_error: 0.0152 26/126 [=====>........................] - ETA: 0s - loss: 2.2046e-04 - mean_absolute_error: 0.0112 52/126 [===========>..................] - ETA: 0s - loss: 1.6522e-04 - mean_absolute_error: 0.0097 76/126 [=================>............] - ETA: 0s - loss: 1.4596e-04 - mean_absolute_error: 0.0091 99/126 [======================>.......] - ETA: 0s - loss: 1.3889e-04 - mean_absolute_error: 0.0089123/126 [============================>.] - ETA: 0s - loss: 1.4390e-04 - mean_absolute_error: 0.0090126/126 [==============================] - 0s 2ms/step - loss: 1.4468e-04 - mean_absolute_error: 0.0090 - val_loss: 1.3098e-04 - val_mean_absolute_error: 0.0099
Epoch 86/100
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Epoch 87/100
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Epoch 88/100
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Epoch 89/100
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Epoch 90/100
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Epoch 91/100
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Epoch 92/100
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Epoch 93/100
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Epoch 94/100
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Epoch 95/100
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Epoch 96/100
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Epoch 97/100
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Epoch 98/100
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Epoch 99/100
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Epoch 100/100
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<keras.src.callbacks.History at 0x2115c0b4610>

After the training and fitting of the Machine Learning model for Switzerland, I tried to create visualizations comparing the model against the country’s training dataset, validation dataset, but most importantly the testing dataset (as shown below in the line graphs). Note that the darker and thicker blue lines represent the prediction model’s projections and the thinner red lines is the observed/gathered data.

```{python}
# Testing the Machine Learning Model prediction for Switzerland with the train, 
# validation, and test sets
# Most important is the test set prediction as this tests the effectiveness
# of the Machine Learning model on data it has not seen before 
switzerland_train_pred = switzerland_model.predict(X_switzerland_train).flatten()

plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4)
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.legend(["Training Predictions", "Training Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Training Set Predictions vs. Observations")
plt.show()

switzerland_val_pred = switzerland_model.predict(X_switzerland_val).flatten()

plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.legend(["Validation Predictions", "Validation Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Validation Set Predictions vs. Observations")
plt.show()

switzerland_test_pred = switzerland_model.predict(X_switzerland_test).flatten()

plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)
plt.legend(["Testing Predictions", "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Switzerland - Testing Set Predictions vs. Observations")
plt.show()
```

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Through careful consideration of all of the prediction-based vs. observation-based contrast visualizations together, I consolidated all of graphics into one singular visualization for you to see below to get a more general perspective of the effectiveness of the Machine Learning model at training and fitting towards predicting Switzerland’s international currency rate with the United States.

```{python}
# Plotting Switzerland's observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model) 
plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4)
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1)
plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4)
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1)
plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4)
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1)

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Switzerland's Dataset - Predictions vs. Observations")
plt.show()
```

To put it all together, I made one large visualization to encompass all 4 countries’ Machine Learning model prediction-based vs. observation-based visualizations - Austrailia, Canada, the United Kingdom, and Switzerland as shown below.

```{python}
# Plotting all countries' observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model in an overall sense) 
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1, linestyle="solid")

plt.plot(dates_canada_train, canada_train_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_train, y_canada_train, linewidth=1, linestyle="dashed")
plt.plot(dates_canada_val, canada_val_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_val, y_canada_val, linewidth=1, linestyle="dashed")
plt.plot(dates_canada_test, canada_test_pred, linewidth=4, linestyle="dashed")
plt.plot(dates_canada_test, y_canada_test, linewidth=1, linestyle="dashed")

plt.plot(dates_united_kingdom_train, united_kingdom_train_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_train, y_united_kingdom_train, linewidth=1, linestyle="dotted")
plt.plot(dates_united_kingdom_val, united_kingdom_val_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_val, y_united_kingdom_val, linewidth=1, linestyle="dotted")
plt.plot(dates_united_kingdom_test, united_kingdom_test_pred, linewidth=4, linestyle="dotted")
plt.plot(dates_united_kingdom_test, y_united_kingdom_test, linewidth=1, linestyle="dotted")

plt.plot(dates_switzerland_train, switzerland_train_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_train, y_switzerland_train, linewidth=1, linestyle="dashdot")
plt.plot(dates_switzerland_val, switzerland_val_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_val, y_switzerland_val, linewidth=1, linestyle="dashdot")
plt.plot(dates_switzerland_test, switzerland_test_pred, linewidth=4, linestyle="dashdot")
plt.plot(dates_switzerland_test, y_switzerland_test, linewidth=1, linestyle="dashdot")

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations"])
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of All Four Countries' Datasets - Predictions vs. Observations")
plt.show()
```

Ultimately, here is the moment of truth: I tried to recursively predict Austrailia’s future expected projection on its international currency rate between the United States through using my LSTM prediction model as shown below. By using the data from the training dataset for Austrailia, I tried to contrast the effectiveness of the model by constrasting its projection to the actual validation and testing data results. However, unfortunately, it did not predict as well as I hoped, but this was a good experiment, nonetheless.

```{python}
# Attempt at using the model to predict into the future for Austrailia's currency 
# rate
recursive_pred: list = []
recursive_dates = np.concatenate([dates_austrailia_val, dates_austrailia_test])

for target_date in recursive_dates:
    last_window = deepcopy(X_austrailia_train[-1])
    next_pred = austrailia_model.predict(np.array([last_window])).flatten()
    recursive_pred.append(next_pred)
    last_window[-1] = next_pred
```

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1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 15ms/step

To visualize the results of my recursive prediction that I completed, I added these results to the previous large visualization that encompassed all 4 countries’ Machine Learning model prediction-based vs. observation-based visualizations - Austrailia, Canada, the United Kingdom, and Switzerland as shown below.

```{python}
# Plotting all countries' observational (reference) data with the predictions 
# of its Machine Learning Model (as a way to visually inspect the effectiveness 
# of the model in an overall sense) along with the new recursive prediction
# results
plt.plot(dates_austrailia_train, austrailia_train_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_train, y_austrailia_train, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_val, austrailia_val_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_val, y_austrailia_val, linewidth=1, linestyle="solid")
plt.plot(dates_austrailia_test, austrailia_test_pred, linewidth=4, linestyle="solid")
plt.plot(dates_austrailia_test, y_austrailia_test, linewidth=1, linestyle="solid")
plt.plot(recursive_dates, recursive_pred, linewidth=4, linestyle="solid")

plt.legend(["Training Predictions",
            "Training Observations",
            "Validation Predictions",
            "Validation Observations",
            "Testing Predictions",
            "Testing Observations",
            "Recursive Predictions"], loc="upper right")
plt.rcParams["font.size"] = 8
plt.tight_layout()
plt.xlabel("Historical Date")
plt.ylabel("Currency Rate of the Respective Country to USD")
plt.title("Culmination of the Three Machine Learning Phases of Austrailia's Dataset - Predictions vs. Observations (With Recursive Pred. Attempt)")
plt.show()
```

Conclusions

• Returning to my attempt to test the ability for my Machine Learning model to predict the International Currency rates for Austrailia from just the range of my training dataset as shown above, I felt that its prediction was acceptable in my opinion giving the averaging across the currency rate values in my validation and testing datasets would have proven to be considerably close in its precision.

• Thus, I reached the conclusion that my International Currency Predictor would not be a viable Machine Learning model for any real-life applications. This Machine Learning model raised a valuable point - financial market prediction is a difficult endeavor to accurately predict. From a more general point of view, this International Currency Maching Learning prediction model is not totally inaccurate or untrustworthy but rather it is only on the micro-scale. These financial predictions do not always account for current events in that respective country such as changing domestic or international relations or constantly changing socioeconomic trends which could significantly sway market confidence and impact currency rate fluctuation. Consequently, economists often have to look at larger economic trends across several decades in order to make more educated predictions.

• Ultimately, I learned a great deal about the complexities of financial prediction and usage of Machine Learning models as an attempt to solve this issue. Even though my model was not as successful at predicting as I would have hoped, I am interested to see the future of Machine Learning as it soon dominates these economic sub-field and continues to rise in its relevance by being a pivotal talking point for the future and applied soon enough to other sectors of our society.

Reference Sources and Citations (IEEE Format)

To complete this blog post, I used the following online sources as references for developing this:

[1] US International Exchange Rates Dataset:

B. Ferreira, “Foreign Exchange Rates 2000-2019”, 2019. [Online]. Available: https://www.kaggle.com/datasets/brunotly/foreign-exchange-rates-per-dollar-20002019. [Accessed: 04-Sep.-2023].

[2] Tutorial on Basics of LSTM and Prediction & Forecasting in Machine Learning:

Greg Hogg, “Stock Price Prediction & Forecasting with LSTM Neural Networks in Python”, YouTube, 26-Mar.-2022. [Online.] Available: https://www.youtube.com/watch?v=CbTU92pbDKw. [Accessed: 06-Sep.-2023].